Mysterious Areas In Mantle Are Slowing Down Seismic Waves
This seismic scattering has long puzzled scientists, but a new study pinpoints where more of these mysterious areas may be hiding.
- Scientists have known that seismic waves slow down when passing through ultra-low velocity zones, or ULVZs, but only knew they existed around hotspots that create volcanic island chains.
- Now, a new study suggests that ULVZs near the core-mantle boundary (CMB) could be responsible for precursor waves of a particular seismic path called PKP waves.
- The hypothesis is that these ULVZs scatter seismic waves, which allowed scattered waves to arrive before the primary wave.
Understanding the interior of the planet is tricky business, but one of the primary methods of probing the Earth’s crust, mantle , and core is to measure seismic waves as they travel through these planetary layers . What scientists have discovered over the decades is that the Earth is a roiling ball of internal complexity, and one of the most head scratching of these deep-earth formations are areas known as “ultra-low velocity zones,” ULVZs.
As their names suggest, ULVZs slow down seismic waves, and scientists have known about these strange areas that tend to hang around hotspots, which are regions where mantle wells up through the crust and forms volcanic chains, such as Hawaii. A new study published in the journal AGU Advances examines how ULVZs located near the core-mantle boundary (CMB) slow down seismic waves by a staggering 50 percent and could help explain a deep Earth mystery that’s puzzled scientists for decades called PKP waves.
University of Utah’s Michael Thorne, lead author of the new study, originally began this study not thinking about ULVZs at all. Instead, he wanted to figure out why PKP waves are preceded by a precursor wave. First, a brief explanation. There are actually many types of seismic core phases that traverse a specific ray path. For example, PcP waves begin as a primary wave (P-wave) and deflect at the CMB and return as a P-wave.
Other phases include such acronyms as PcS, PKIKP, PKJKP. and many others for describe specific path a wave might take. The PKP acronym, which represent the kind of waves that were central to this study, describe a P-wave that travels through the mantle, the outer core, and the mantle again on the other side (when P-waves are in the outer core they’re labeled “K,” hence PKP).
With all that alphabet soup aside, scientists have noticed for decades that these waves have a strange quality as precursor signals would arrive before the main P-wave, suggesting that the waves were being scattered, but scientists weren’t sure by what. By using a seismic array method, observations from theoretical earthquake simulations, and data from 58 earthquakes that occurred in New Guinea and were recorded in North America, Thorne and his team were able piece together that there must be ULVZs occurring along the top of the CMB.
“What we’ve now found is that these ultra-low velocity zones do not just exist beneath the hotspots,” Thorne said in a press statement . “They’re spread out all across the core-mantle boundary beneath North America. Because we’re seeing them near subduction, we think mid-ocean ridge basalts are getting melted, and that is how it’s getting generated. And then the dynamics is pushing these things all across Earth, and ultimately they’re going to accumulate beneath the hotspots.”
Unlocking the secrets of ULVZs could help scientists better understand hotspot formation as well as the dynamics of Earth’s mantle. Now that scientists know they exist around the CMB, its up to future studies to understand how their existence might impact Earth’s geologic processes.
“These are some of the most extreme features discovered on the planet,” Thorne said in a press statement. “We legitimately do not know what they are.”
Darren lives in Portland, has a cat, and writes/edits about sci-fi and how our world works. You can find his previous stuff at Gizmodo and Paste if you look hard enough.
A New Study Unravels a Major Microfossil Mystery
Can Some Electroshock Therapy Save Our Beaches?
Scientists Find Incredible Proof of Snowball Earth
A ‘Mercury Bomb' Is Looming in a Warming Arctic
Study Adds Up the Probability of Climate Collapse
Could the Earth Actually Have Six Continents?
This Ancient Larva Still Has Its Brain And Guts
Scientists Go Deeper Into Mantle Than Ever Before
Waves in the Mantle Are Causing Continents to Rise
Amazon Captures More Carbon Thanks to ‘Dark Earth'
AUV Reveals Stunning Images of Dotson Ice Shelf
Life Might’ve Had a Pretty Electrifying Beginning
(NAS Colloquium) Earthquake Prediction: The Scientific Challenge (1996)
Chapter: hypothesis testing and earthquake prediction.
This paper was presented at a colloquium entitled “Earthquake Prediction: The Scientific Challenge,” organized by Leon Knopoff (Chair), Keiiti Aki, Clarence R.Allen, James R.Rice, and Lynn R.Sykes, held February 10 and 11, 1995, at the National Academy of Sciences in Irvine, CA.
Hypothesis testing and earthquake prediction
(probability/significance/likelihood/simulation/Poisson)
D AVID D.J ACKSON
Southern California Earthquake Center, University of California, Los Angeles, CA 90095–1567
ABSTRACT Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of “normal behavior”— that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earthquakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.
Hypothesis testing is an essential part of the scientific method, and it is especially important in earthquake prediction because public safely, public funds, and public trust are involved. However, earthquakes occur apparently at random, and the larger, more interesting earthquakes are infrequent enough that a long time may be required to test a hypothesis. For this reason, it is important to formulate hypotheses carefully so that they may be reasonably evaluated at some future time.
Single Prediction
The simplest definition of earthquake prediction involves specification in advance of the time interval, region, and magnitude range in which a future earthquake is predicted to occur. To be meaningful, all of these ranges should be defined in such a way that any future earthquake could be objectively judged to be either inside or outside the range. In addition, some definition should be given for the “location” of an earthquake, since an earthquake does not occur at a point, and the region should be specified in three dimensions, because deep earthquakes may be different in character from shallow earthquakes. An example of a testable earthquake prediction is as follows:
“An earthquake with moment magnitude equal to or greater than 6.0 will occur between 00:00 January 1, 1996, and 00:00 January 1, 1997, with hypocenter shallower than 20 km, within the latitude range 32.5 degrees N and 40.0 degrees north, and within the longitude range 114 W to 125 W. The moment magnitude shall be determined from the most recent version of the Harvard Central Moment tensor catalog as of July 1, 1997, and the hypocenter shall be determined by the most recent version of the Preliminary Determination of Epicenters (PDE) catalog as of July 1, 1997. All times are Greenwich Mean Times.”
This definition illustrates the difficulty of giving even a simple definition of a predicted event: there are different magnitude scales, different listings of hypocenters, and different time zones. If these are not specified in advance, one cannot objectively state that some future earthquake does or does not satisfy the prediction. An earthquake that meets the definition of a predicted event may be called a qualifying earthquake. In the discussion to follow, I will usually drop the adjective; the term earthquake should be interpreted as a qualifying earthquake. The example illustrates another problem: the prediction may not be very meaningful, because the area is fairly large (it includes all of California) and fairly active. The occurrence of an earthquake matching the prediction would not be very conclusive, because earthquakes satisfying the size and location conditions occur at the rate of about 1.5 per year. Thus, a single success of this type would not convincingly validate the theory used to make the prediction, and knowing the background rate is important in evaluating the prediction. One can then compare the observed earthquake record (in this case, the occurrence or not of a qualifying earthquake) with the probabilities for either case according to a “null hypothesis,” that earthquakes occur at random, at a rate determined by past behavior. In this example, we could consider the null hypothesis to be that earthquakes result from a Poisson process with a rate of r= 1.5/yr; the probability that at least one qualifying earthquake would occur at random is
p 0 = 1 − exp (−r*t) .
For r =1.5/yr and t =1 yr, p 0 = 0.78. [1]
What can be said if a prediction is not satisfied? In principle, one could reject the prediction and the theory behind it. In practice, few scientists would completely reject a theory for one failure, no matter how embarrassing. In some cases, a probability is attached to a simple prediction; for the Parkfield, California, long-term prediction ( 1 ), this probability was taken to be 0.95. In such a case the conclusions that can be drawn from success depend very much on the background probability, and only weak conclusions can be drawn from failure. In the Parkfield case, the predicted event was never rigorously defined, but clearly no qualifying earthquake occurred during the predicted time interval (1983–1993). The background rate of Parkfield earthquakes is usually taken to be 1/22 yr; for t = 10 yr, p 0 =0.37. Thus, a qualifying earthquake, had it occurred, would not have been sufficient evidence to reject the Poissonian null hypothesis. Most scientists involved in the
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Parkfield prediction have responded to the failed prediction by modifying the parameters, rather than by abandoning the entire theory on which it was founded.
Some predictions (like Parkfield) are intended to be terminated as soon as they are satisfied by a qualifying earthquake, while others, such as those based on earthquake clustering, might be “renewed” to predict another earthquake if a qualifying earthquake occurs. Thus, when beginning an earthquake prediction test, it is helpful to describe in advance any conditions that would lead to a termination of the test.
Multiple Predictions
Suppose there are P predictions, either for separate regions, separate times, separate magnitude ranges, or some combination thereof. To simplify the following discussion, we refer to the magnitude-space-time interval for each prediction as a “region.” Let p 0 j , for ( i =1,…, P ), be the random probabilities of satisfying the predictions in each region, according to the null hypothesis, and let c i for ( i =1,…, P ), be 1 for each region that is “filled” by a qualifying earthquake, and 0 for those not filled. Thus, c i is 1 for each successful prediction and zero for each failure. According to this scheme only the first qualifying earthquake counts in each region, so that implicitly the prediction for each region is terminated as soon as it succeeds. A reasonable measure of the success of the predictions is the total number of regions filled by qualifying earthquakes. The probability of having as many or more successes at random is approximately that given by the Poisson distribution
where λ is the expected number of successes according to the null hypothesis. The choice of the rate parameter λ requires some care. If multiple events in each region are to be counted separately, then λ is simply the sum of the rates in the individual regions, multiplied by the time interval. If, instead, only the first earthquake within each region counts, then λ is the sum of the probabilities, over all regions, of success during the time interval t . The difference between the two approaches is small when all rates are small, so that there is very little chance of having more than one event in a region. The difference becomes important when some of the rates are high; in the first approach, the sum of the rates may exceed P/t, whereas this is prohibited in the second approach because each region may have only one success. The first approach is appropriate if the physical hypothesis predicts an enhanced rate of activity that is expected to continue after the first qualifying event in each region. The second approach is more appropriate when the hypothesis deals only with the first event, which changes the physical conditions and the earthquake rates. An advantage of the second approach is that it is less sensitive to treatment of aftershocks. Under the first approach, aftershocks must be specifically included in the prediction model or else excluded from the catalog used to make the test. Whether aftershocks are explicitly predicted or excluded from the catalog, the results of the test may be very sensitive to the specific algorithm used to predict or recognize aftershocks.
Eq. 2 is approximate, depending on λ being large compared to one. There are also a few other, more subtle, assumptions. A more robust, but also approximate, estimate of the probabilities may be obtained by simulation. Assume the second approach above, that the prediction for each region is either no earthquake, or at least one. Draw at random a large number of simulated catalogs, also represented by c i for ( i =1,…, P ). For each catalog, for each region, draw a random number from a uniform distribution between 0 and 1; if that random number is less than p 0 j , then c i =1; otherwise, c i =0. For each synthetic catalog, we count the number of successes and compile a cumulative distribution of that number for all synthetic catalogs. Then the proportion of simulated catalogs having N or more events is a good estimate of the corresponding probability. Simulation has several advantages: it can be tailored to specific probability models for each zone, it can be used to estimate other statistics as discussed below, and its accuracy depends on the number of simulations rather than the number and type of regions. Disadvantages are that it requires more computation, and it is harder to document and verify than an analytic formula like Eq. 2.
The “M8” prediction algorithm of Keilis-Borok and Kossobokov ( 2 ) illustrates some of the problems of testing prediction hypotheses. The method identifies five-year “times of increased probabilities,” or TIPs, for regions based largely on the past seismic record. Regions are spatially defined as the areas within circles about given sites, and a lower magnitude threshold is specified. TIPs occur at any time in response to earthquake occurrence; thus, the probabilities are strongly time dependent. The method apparently predicted 39 of 44 strong earthquakes over several regions of the world, while the declared TIPs occupied only 20% of the available space-time. However, this apparently high success rate must be viewed in the context of the normal seismicity patterns. Most strong earthquakes occur within a fairly small fraction of the map, so that some success could be achieved simply by declaring TIPs at random times in the more active parts of the earth. A true test of the M8 algorithm requires a good null hypothesis that accounts for the spatial variability of earthquake occurrence. Constructing a reasonable null hypothesis is difficult because it requires the background or unconditional rate of large earthquakes within the TIP regions. However, this rate is low, and the catalog available for determining the rate is short. Furthermore, the meaning of a TIP is not explicit: is it a region with higher rate than other regions, or higher than for other times within that region? How much higher than normal is the rate supposed to be?
Probabilistic Prediction
An earthquake prediction hypothesis is much more useful, and much more testable, if probabilities are attached to each region. Let the probabilities for each region be labeled p j , for j =1 through P. For simplicity, I will henceforth consider the case in which a prediction for any region is terminated if it succeeds, so that the only possibility for each region is a failure (no qualifying earthquake) or a success (one or more qualifying earthquakes). In this case, several different statistical tests are available. Kagan and Jackson ( 3 ) discuss three of them, applying them to the seismic gap theory of Nishenko ( 4 ). Papadimitriou and Papazachos ( 5 ) give another example of a long-term prediction with regional probabilities attached.
(i) The “ N test,” based on the total number of successes. This number is compared with the distributions predicted by the null hypothesis (just as described above) and by the experimental hypothesis to be tested. Usually, the null hypothesis is based on the assumption that earthquake occurrence is a Poisson process, with rates determined by past behavior, and the test hypothesis is that the rates are significantly higher in some places. Two critical values of N can be established. Let N 1 be the smallest value of N, such that the probability of N or more successes according to the null hypothesis is less than 0.05. Then the null hypothesis can be rejected if the number of successes in the experiment exceeds N 1 . Let N 2 be the largest value of N, such that the probability of N or fewer successes according to the test hypothesis is less than 0.05. Then the test hypothesis can be rejected if N is less than or equal to N 2 . If N 1 is less than N 2 , then there is a range of possible success counts for which neither hypothesis can be rejected. According to classical hypothesis testing methodology, one does not
accept the test hypothesis unless the null hypothesis is rejected. This practice is based on the notion that the null hypothesis is inherently more believable because of its simplicity. However, there is not always uniform agreement on which hypothesis is simpler or inherently more believable. The method presented here gives no special preference to the null hypothesis, although such preference can always be applied at the end to break ties. If N 1 exceeds N 2 , there will be a range of possible outcomes for which both hypotheses may be rejected.
Under a few simple assumptions, L will be normally distributed with a mean and variance readily calculable from Eq. 3. Alternatively, simulated catalogs c j may be constructed as described above, and the distribution of L may be estimated by the simulated sample distribution. The hypothesis test is made by comparing the value of L, using the actual earthquake record, with the distribution of simulated catalogs. If L for the actual catalog is less than 95% of the simulated values, then the hypothesis should be rejected. The L test may be applied to both the test hypothesis and the null hypothesis separately. Either, neither, or both might be rejected.
(iii) The “ R test,” based on the ratio of the likelihood value of the test hypothesis to that of the null hypothesis. The log of this ratio is
R is the difference between two log-likelihood functions, with the test hypothesis having the positive sign and the null hypothesis the negative sign. Thus, positive values favor the test hypothesis, and negative values favor the null hypothesis. The hypothesis test involves calculating the distributions of R for the test hypothesis (that is, assuming that c j is a sample of the process with probabilities p j ), and for the null hypothesis (assuming the c j correspond to p j ). The log-likelihood ratio will be normally distributed if the log-likelihood functions for both the test and null hypotheses, calculated using Eq. 3, are normally distributed. Alternatively, the distributions may be calculated as follows: First, generate simulated catalogs using the probabilities p j . Then compute the log-likelihood ratio for each catalog using Eq. 4 and compute the sample distribution of log-likelihood values. Now generate a suite of simulated catalogs using p 0 j , the probabilities for the null hypothesis. For each of these, calculate R from Eq. 4 and compute their distribution. Again, two critical values may be calculated. Let R 1 be the least value of R, such that less than 5% of the catalogs simulated using the null hypothesis have a larger value of R . Then the null hypothesis can be rejected with 95% confidence if the actual value of R exceeds R 1 . Let R 2 be the largest value of R, such that 5% or less of the catalogs simulated using the test hypothesis have a smaller value of R . Then the test hypothesis can be rejected with 95% confidence if the actual value of R is less than R 2 . As in the other tests, either, neither, or both of the two hypotheses may fail the test.
Let us now revisit the topic of predictions in which no probability is given for the test hypothesis. These might be interpreted as identifying regions for which the probability of an earthquake is larger than that for other regions or in which the ratio of probability according to the test hypothesis to that of the null hypothesis is especially high. These predictions are often discussed in terms of a trade-off curve, in which the fraction of earthquakes predicted is compared to the fraction of space-time for which “alarms” are active. As more of time-space is covered by predictions, more earthquakes will be successfully predicted. Success of a prediction scheme is measured by comparing it with a hypothetical scheme which occupies the same proportion of space-time. An optimum threshold can be calculated if costs can be assigned to maintaining a state of alert and to unpredicted earthquakes. However, if probabilities are given for each region, then a prediction scheme can be tested using all of the probabilities, and there is no need to establish a threshold for declaring alerts. This separates the problem of testing scientific hypotheses from the policy problem of how to respond to predictions.
Predictions Based on Rate-Density Maps
Predictions based on subdividing magnitude-time-space into regions have the disadvantage that probabilities may differ strongly from one region to another, so the success or failure of a hypothesis may depend strongly on small errors in location or magnitude estimates. Furthermore, earthquakes that occur just outside the boundaries of a high probability region pose a strong temptation to bend the boundaries or adjust the earthquake data. Statistical procedures should account for uncertainty in measurement and for imperfect specificity in stating hypotheses. One way to achieve these goals is to formulate hypotheses in terms of smaller regions, allowing for some smoothing of probability from one region to another. In fact, many prediction hypotheses lend themselves to description in terms of continuous variables, such as functions of distance from previous earthquakes or locations of geophysical anomalies. Let us consider a refinement of the “region” concept to include infinitesimal regions of time-space, in which p j =∧ (x j , y j , t j , m j ) dx dy dt dm. Here dx, dy, dt, and dm are small increments of longitude, latitude, time, and magnitude, respectively. The function ∧ is often referred to in the statistical literature as the “conditional intensity,” but I will use the notation “conditional rate density” to avoid confusion with the seismic intensity. The term “conditional” is used because earthquake prediction usually refers to a temporal increase in earthquake likelihood inferred from the existence of some special geophysical circumstance. If dx dy dt dm is made arbitrarily small, then 1− p j approaches 1, and log(1− p j ) approaches −pj . After some algebra, we then obtain
where N is the number of earthquakes that actually occurred, N' is the total number of earthquakes predicted by the test hypothesis, and N′ 0 is the total number of earthquakes predicted by the null hypothesis. The sum is over those earthquakes that actually occurred.
A consequence of shrinking the regions to an infinitesimal size is that there is no longer any reason to distinguish between the first and later earthquakes in a region; all of the regions are so small that the probability of a second event in a given box is also infinitesimal. Another consequence is that aftershocks must be dealt with carefully. No longer is it possible to assume that they will be unimportant if we consider only the first
qualifying earthquake in a region. Now it is virtually certain that the aftershocks from an earthquake in one region will occur in another. One way to deal with aftershocks is to use a declustered catalog in making the test. Another is to build aftershock prediction into both the test and null hypotheses. Kagan and Knopoff ( 6 ) offer an algorithm for modeling the probability of earthquakes following an earlier quake. Testing a hypothesis on “new data”—that is, using a hypothesis formulated before the occurrence of the earthquakes on which it is tested—now requires that the hypothesis be updated very quickly following a significant earthquake. Rapid updating presents a practical problem, because accurate data on the occurrence of important earthquakes may not be available before significant aftershocks occur. One solution is to specify in advance the rules by which the hypothesis will be updated and let the updating be done automatically on the basis of preliminary information. A more careful evaluation of the hypothesis may be carried out later with revised data, as long as no adjustments are made except as specified by rules established in advance.
Introducing aftershocks into the model makes it more difficult to compute confidence limits for the log-likelihood ratio than would otherwise be the case. Because the rate density may change dramatically at some time unknown at the beginning of a test period, it is very difficult to compute even the expected numbers of events analytically unless the aftershock models are very simple.
Rhoades and Evison ( 7 ) give an example of a method, based on the occurrence of earthquake swarms, in which the prediction is formulated in terms of a conditional rate density. They are currently testing the method against a Poissonian null hypothesis in New Zealand and Japan. They update the conditional rate calculations to account for earthquakes as they happen. As discussed above, it is particularly difficult to establish confidence limits for this type of prediction experiment.
A fair test of an earthquake prediction hypothesis must involve an adequate null hypothesis that incorporates well-known features of earthquake occurrence. Most seismologists agree that earthquakes are clustered both in space and in time. Even along a major plate boundary, some regions are considerably more active than others. Foreshocks and aftershocks are manifestations of temporal clustering. Accounting for these phenomena quantitatively is easier said than done. Even a Poissonian null hypothesis requires that the-rate density be specified as a function of spatial variables, generally by smoothing past seismicity. Choices for the smoothing kernel, the time interval, and the magnitude distribution may determine how well the null hypothesis represents future seismicity, just as similar decisions will affect the performance of the test hypothesis. In many areas of the world, available earthquake catalogs are insufficiently complete to allow an accurate estimate of the background rate. Kagan and Jackson ( 8 ) constricted a global average seismicity model based on earthquakes since 1977 reported in the Harvard catalog of central moment tensors. They determined the optimum smoothing for each of several geographic regions and incorporated anisotropic smoothing to account for the tendency of earthquakes to occur along faults and plate boundaries. They did not incorporate aftershock occurrence into their model, and the available catalog covers a relatively short time span. In many areas, it may be advantageous to use older catalogs that include important large earthquakes, even though the data may be less accurate than that in more recent catalogs. To date, there is no comprehensive treatment of spatially and temporally varying seismicity that can be readily adapted as a null hypothesis.
Binary (on/off) predictions can be derived from statements of conditional probability within regions of magnitude-time-space, which can in turn be derived from a specification of the conditional rate density. Thus, the conditional rate density contains the most information. Predictions specified in this form are no more difficult to test than other predictions, so earthquake predictions should be expressed as conditional rate densities whenever possible.
I thank Yan Kagan, David Vere-Jones, Frank Evison, and David Rhoades for useful discussions on these ideas. This work was supported by U.S. Geological Survey Grant 1434–94-G-2425 and by the Southern California Earthquake Center through P.O. 566259 from the University of Southern California. This is Southern California Earthquake Center contribution no. 321.
1. Bakun, W.H. & Lindh, A.G. (1985) Science 229 , 619–624.
2. Keilis-borok, V.I. & Kossobokov, V.G. (1990) Phys. Earth Planet. Inter. 61 , 73–83.
3. Kagan, Y.Y. & Jackson, D.D. (1995) J. Geophys. Res. 100 , 3943–3959.
4. Nishenko, S.P. (1991) Pure Applied Geophys. 135 , 169–259.
5. Papadimitriou, E.E. & Papazaehos, B.C. (1994) J. Geophys. Res. 99 , 15387–15398.
6. Kagan, Y.Y. & Knopoff, L. (1987) Science 263 , 1563–1567.
7. Rhoades, D.A. & Evison, F.F. (1993) Geophys. J. Int. 113 , 371–381.
8. Kagan, Y.Y. & Jackson, D.D. (1994) J. Geophys. Res. 99 , 13685–13700.
READ FREE ONLINE
Welcome to OpenBook!
You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.
Do you want to take a quick tour of the OpenBook's features?
Show this book's table of contents , where you can jump to any chapter by name.
...or use these buttons to go back to the previous chapter or skip to the next one.
Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.
Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.
To search the entire text of this book, type in your search term here and press Enter .
Share a link to this book page on your preferred social network or via email.
View our suggested citation for this chapter.
Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.
Get Email Updates
Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.
Characteristic Earthquakes and Seismic Gaps
- Reference work entry
- First Online: 01 January 2021
- Cite this reference work entry
- David D. Jackson 2 &
- Yan Y. Kagan 2
Part of the book series: Encyclopedia of Earth Sciences Series ((EESS))
359 Accesses
Relative motion, either steady or sudden as in earthquakes, between rock units on either side of a fault.
Sudden fault slip in an earthquake.
Sudden release, as by an earthquake, of slowly accumulated strain energy.
A section of a fault or plate interface bounded by features thought to serve as strong barriers to earthquake rupture. Features postulated to form such barriers include changes in fault orientation or in rock type across parts of the fault, and intersections with other faults.
An earthquake rupturing an entire fault segment. Alternately, one of a sequence of earthquakes rupturing the same area of fault.
The time between characteristic earthquakes on a given segment or fault area. Quasiperiodic
Occurring at approximately equal recurrence intervals.
A sequence of events on a segment starting with a large earthquake, followed by aftershocks, then by steady...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Subscribe and save.
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
- Available as PDF
- Read on any device
- Instant download
- Own it forever
- Available as EPUB and PDF
- Durable hardcover edition
- Dispatched in 3 to 5 business days
- Free shipping worldwide - see info
Tax calculation will be finalised at checkout
Purchases are for personal use only
Institutional subscriptions
Bibliography
Fedotov SA (1965) Regularities of the distribution of strong earthquakes in Kamchatka, the Kurile Islands and northeastern Japan. Tr Inst Fiz Zemli Akad Nauk SSSR 36(203):66–93 (in Russian)
Google Scholar
Gilbert GK (1884) A theory of the earthquakes of the Great Basin, with a practical application. Am J Sci 27(157):49–54. Ser. 3
Article Google Scholar
Igarashi T, Matsuzawa T, Hasegawa A (2003) Repeating earthquakes and interplate aseismic slip in the northeastern Japan subduction zone. J Geophys Res 108(B5):2249. https://doi.org/10.1029/2002JB001920
Jackson DD, Kagan YY (2006) The 2004 parkfield earthquake, the 1985 prediction, and characteristic earthquakes: lessons for the future. Bull Seismol Soc Am 96:S397–S409
Kagan YY, Jackson DD (1991) Seismic gap hypothesis: ten years after. J Geophys Res 96(21):21419–21431
Kagan YY, Jackson DD (1995) New seismic gap hypothesis: five years after. J Geophys Res 100(B3):3943–3959
Kasahara K (1981) Earthquake mechanics. Cambridge University Press, Cambridge
Kelleher JA (1972) Rupture zones of large South American earthquakes and some predictions. J Geophys Res 77:2087–2103
Kelleher JA, Sykes LR, Oliver J (1973) Possible criteria for predicting earthquake locations and their applications to major plate boundaries of the Pacific and Caribbean. J Geophys Res 78:2547–2585
McCann WR, Nishenko SP, Sykes LR, Krause J (1979) Seismic gaps and plate tectonics: seismic potential for major boundaries. Pure Appl Geophys 117:1082–1147
Nadeau RM, McEvilly TV (1999) Fault slip rates at depth from recurrence intervals of repeating microearthquakes. Science 285:718–721
Nalbant S, Steacy S, Sieh K, Natawidjaja D, McCloskey J (2005) Seismology: earthquake risk on the Sunda trench. Nature 435(7043):756–757
Nishenko SP (1989) Earthquakes: hazards and predictions. In: James DE (ed) The encyclopedia of solid earth geophysics. Van Nostrand Reinhold, New York, pp 260–268
Nishenko SP (1991) Circum-Pacific seismic potential – 1989–1999. Pure Appl Geophys 135:169–259
Okada T, Matsuzawa T, Hasegawa A (2003) Comparison of source areas of M4.8 ± 0.1 repeating earthquakes off Kamaishi, NE Japan: are asperities persistent features? Earth Planet Sci Lett 213:361–374
Reid HF (1910) The California earthquake of April 18, 1906. The mechanics of the earthquake, vol 2. Carnegie Institution of Washington, Washington, DC
Reid HF (1911) The elastic-rebound theory of earthquakes. Univ Calif Dep Geol Bull 6:413–444
Rong Y-F, Jackson DD, Kagan YY (2003) Seismic gaps and earthquakes. J Geophys Res 108(B10):2471, ESE-6, 1–14
Scholz C (1990) The mechanics of earthquakes and faulting. Cambridge University Press, Cambridge
Sykes LR (1971) Aftershock zones of great earthquakes, seismicity gaps, and earthquake prediction for Alaska and the Aleutians. J Geophys Res 76:8021–8041
Working Group on California Earthquake Probabilities (1988) Probabilities of large earthquakes occurring in California, on the San Andreas Fault. U.S. Geological Survey Open-File Report, 88–398, 62 ~ pp
Working Group on California Earthquake Probabilities (1990) Probabilities of large earthquakes in the San Francisco Bay Region, California, USGS Circular 1053
Working Group on California Earthquake Probabilities (1995) Seismic hazards in southern California: probable earthquakes, 1994–2024. Bull Seismol Soc Am 85:379–439
Working Group on California Earthquake Probabilities (2002) Earthquake probabilities in the San Francisco Bay region: 2002 to 2031, USGS Circular 1189
Working Group on California Earthquake Probabilities (2008) The Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2): U.S. Geological Survey Open-File Report 2007–1437 and California Geological Survey Special Report 203. http://pubs.usgs.gov/of/2007/1437/
Download references
Author information
Authors and affiliations.
Department of Earth and Space Sciences, University of California Los Angeles, Los Angeles, CA, USA
David D. Jackson & Yan Y. Kagan
You can also search for this author in PubMed Google Scholar
Corresponding author
Correspondence to David D. Jackson .
Editor information
Editors and affiliations.
National Geophysical Research Institute, Council of Scientific and Industrial Research, Hyderabad, India
Harsh K. Gupta
Rights and permissions
Reprints and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this entry
Cite this entry.
Jackson, D.D., Kagan, Y.Y. (2021). Characteristic Earthquakes and Seismic Gaps. In: Gupta, H.K. (eds) Encyclopedia of Solid Earth Geophysics. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-58631-7_181
Download citation
DOI : https://doi.org/10.1007/978-3-030-58631-7_181
Published : 27 May 2021
Publisher Name : Springer, Cham
Print ISBN : 978-3-030-58630-0
Online ISBN : 978-3-030-58631-7
eBook Packages : Earth and Environmental Science Reference Module Physical and Materials Science Reference Module Earth and Environmental Sciences
Share this entry
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
- Publish with us
Policies and ethics
- Find a journal
- Track your research
- Search Menu
- Sign in through your institution
- Volume 239, Issue 1, October 2024 (In Progress)
- Volume 238, Issue 3, September 2024
- Advance Access
- Applied and Marine Geophysics
- General Geophysical Methods
- Geodynamics and Tectonics
- Geomagnetism and Electromagnetism
- Gravity, Geodesy and Tides
- Heat Flow and Volcanology
- Rock and Mineral Physics, Rheology
- Mount Etna Virtual Issue
- Advances in Induced Polarization
- 100 Influential Papers
- Advance Articles
- Express Letters
- Hunga Volcano Special Issue
- East Anatolia Fault Special Issue
- Special Issues
- Why Publish
- Author Guidelines
- Submission Site
- Read & Publish
- Developing Countries Initiative
- Author Resources
- Self-Archiving policy
- Rights and Permissions
- About Geophysical Journal International
- Editorial Board
- About the Royal Astronomical Society
- About the DGG
- Journals on Oxford Academic
- Books on Oxford Academic
Article Contents
- < Previous
Are earthquakes predictable?
- Article contents
- Figures & tables
- Supplementary Data
Yan Y. Kagan, Are earthquakes predictable?, Geophysical Journal International , Volume 131, Issue 3, December 1997, Pages 505–525, https://doi.org/10.1111/j.1365-246X.1997.tb06595.x
- Permissions Icon Permissions
The answer to the above question depends on the definition of earthquake prediction. We discuss several definitions and possible classifications of earthquake prediction methods. We also consider various measures of prediction efficiency, review several recent examples of earthquake prediction, and describe the methods that can be used to verify prediction schemes. We conclude that an empirical search for earthquake precursors that forecast the size of an impending earthquake has been fruitless. Despite considerable effort in several countries, no statistically rigorous validation of proposed precursory phenomena is available: therefore, reported cases of precursors can be explained by random noise or by chance coincidence. We present evidence that earthquakes are non-linear, chaotic, scale-invariant phenomena. The most probable consequence of earthquake self-similarity is a lack of earthquake predictability as popularly defined, that is a forecast of a specific individual earthquake. Many small earthquakes occur throughout any seismic zone, demonstrating that the critical conditions for earthquake nucleation are satisfied almost everywhere. Apparently, any small shock can grow into a large event. Thus, it is likely that an earthquake has no preparatory stage. This sceptical view of current earthquake prediction efforts should not be interpreted as a statement that any further attempts to mitigate the destructive effects of earthquakes are futile. The seismic-moment conservation principle, when combined with geodetic deformation data, offers a new way to evaluate the seismic hazard, not only for tectonic plate boundaries, but also for areas of low seismicity, that is the interiors of continents. Earthquake clustering with a power-law temporal decay (Omori's law) can be used to estimate the rate of future earthquake ocurrence. Realtime seismology can facilitate relief efforts after large earthquakes and eventually provide an immediate warning of severe shaking a few seconds or tens of seconds before the shaking starts.
Abercrombie R.E. Brune J.N. , 1994 . Evidence for a constant b -value above magnitude 0 in the southern San Andreas. San Jacinto and San Miguel fault zones, and at the Long Valley caldera, California , Geophys. Res. Lett. , 21 , 1647 – 1650 .
Google Scholar
Abraham F.F. , 1996 . Parallel simulations of rapid fracture , in Fracture—Instability Dynamics, Scaling and Ductile Brittle Behavior , pp. 311 – 320 , eds Selinger B.R. Mecholsky J.J. Carlsson A.E. Fuller E.R. Jr , Mater. Res. Soc. Pittsburgh, PA.
Google Preview
Abraham F.F. Marder M. Griffiths J.R. Fineberg J. , 1997 . MAAD scientists and others do numerical fracture studies , Physics Today , 50 ( 2 ), 15, 89 – 90 .
Aceves R.L. Park S.K. Strauss D.J. , 1996 . Statistical evaluation of the VAN method using the historic earthquake catalog in Greece , Geophys. Res. Lett. , 23 , 1425 – 1428 .
Agnew D.C. Ellsworth W.L. , 1991 . Earthquake prediction and long-term hazard assessment , Rev. Geophys. Suppl. , 29 , 877 – 889 .
Aki K. , 1981 . A probabilistic synthesis of precursory phenomena , in Earthquake Prediction, An International Review , Maurice Ewing Vol. 4 , pp. 566 – 574 , eds Simpson D.W. Richards P.G. , AGU , Washington, DC.
Aki K. , 1995 . Earthquake prediction, societal implications , Rev. Geoph. , Suppl. , 33 , 243 – 247 .
Anderson P.W. , 1992 . The Reverend Thomas Bayes, needles in haystacks, and the fifth force , Physics Today , 45 ( 1 ), 9 – 11 .
Bak P. , 1996 . How Nature Works: the Science of Self-Organized Criticality , Copernicus , New York, NY.
Bakun W.H. Lindh A.G. , 1985 . The Parkfield, California, earthquake prediction experiment , Science , 229 , 619 – 624 .
Ben-Menahem A. , 1995 . A concise history of mainstream seismology-origins, legacy, and perspectives , Bull. seism. Soc. Am. , 85 , 1202 – 1225 .
Brune J.N. , 1979 . Implications of earthquake triggering and rupture propagation for earthquake prediction based on premonitory phenomena , J. geophys. Res. , 84 , 2195 – 2198 .
Casti J.L. Karlqvist A. , eds. 1996 . Boundaries and Barriers: on the Limits to Scientific Knowledge , Addison-Wesley , Reading, MA.
Cowie P.A. Sornette D. Vanneste C. , 1995 . Multifracture scaling properties of a growing fault population , Geophys. J. Int. , 122 , 457 – 469 .
Daley D.J. Vere-Jones D. , 1988 . An Introduction to the Theory of Point Processes , Springer-Verlag , New York. NY.
DeMets C. Gordon R.G. Argus D.F. Stein S. , 1990 . Current plate motions , Geophys. J. Int. , 101 , 425 – 478 .
Desrochers P.R. Donaldson R.J. , 1992 . Automatic tornado prediction with an improved mesocyclone-detection algorithm , Weather Forecasting , 7 , 373 – 388 .
Dieterich J.H. , 1993 . Comparison of M 8 test results with ‘Poisson’ model , USGS Open-file rept 93 – 333 , Appendix I.
Dziewonski A.M. Ekström G. Salganik M.P. , 1996 . Centroid-moment tensor solutions for July-September 1995 , Phys. Earth planet. Inter. , 97 , 3 – 13 .
von Engelhardt W. Zimmermann J. , 1988 . Theory of Earth Science . University Press , Cambridge.
Evison F.F. Rhoades D.A. , 1993 . The precursory earthquake swarm in New Zealand: hypothesis tests , New Zealand J. Geol. Geophys. , 36 , 51 – 60 ; Correction, 267 .
Evison F.F. Rhoades D.A. 1994 . On the testing of earthquake precursors , in Electromagnetic Phenomena Related to Earthquake Prediction , pp. 1 – 11 , eds Hayakawa M. Fujinawa Y. , Terrapub , Tokyo.
Feng D.Y. Gu J.P. Lin M.Z. Xu S.X. Yu X.J. , 1984 . Assessment of earthquake hazard by simultaneous use of the statistical method and the method of fuzzy mathematics , Pure appl. Geoph. , 122 , 982 – 997 .
Frisch U. , 1995 . Turbulence: Legacy of A. N. Komogorov , Cambridge University Press , Cambridge.
Geller R.J. , 1991 . Shake-up for earthquake prediction . Nature , 352 , 275 – 276 .
Geller R.J. , 1996 . VAN: A critical evaluation , in Critical Review of VAN, Earthquake Prediction from Seismic Electrical Signals , pp. 155 – 238 , ed. Lighthill J. , World Scientific , Singapore.
Geller R.J. , ed., 1996 . Debate on ‘VAN ’, Geophys. Res. Lett. , 23 , 1291 – 1452 .
Geller R.J. , 1997 . Earthquake prediction: a critical review , Geophys. J. Int. , 131 , 425 – 450 (this issue).
Geller R.J. Jackson D.D. Kagan Y.Y. Mulargia F. , 1997 . Earthquakes cannot be predicted , Science , 275 , 1616 – 1617 .
Gross S.J. Rundle J.B. , 1995 . A systematic test of time-to-failure analysis , Eos. Trans. Am. geophys. Un. , 76 ( 46 ), Suppl., F409 .
Harris R.A. Simpson R.W. , 1996 . In the shadow of 1857—the effect of the great Ft Tejon earthquake on subsequent earthquakes in southern California , Geophys. Res. Lett. , 23 , 229 – 232 .
Jackson D.D. , 1996 . Earthquake prediction evaluation standards applied to the VAN method , Geophys. Res. Lett. , 23 , 1363 – 1366 .
Jackson D.D. , 1996 . Hypothesis testing and earthquake prediction , Proc. Nat. Acad. Sci. USA , 93 , 3772 – 3775 .
Jackson D.D. Kagan Y.Y. , 1993 . Reply to Nishenko & Sykes , J. geophys. Res. , 98 , 9917 – 9920 .
Jaumé S.C. Sykes L.R. , 1996 . Evolution of moderate seismicity in the San Francisco Bay region, 1850 to 1993: seismicity changes related to the occurrence of large and great earthquakes . J. geophys. Res. , 101 , 765 – 789 .
Johansen A. Sornette D. Wakita H. Tsunogai U. Newman W.I. Saleur H. , 1996 . Discrete scaling in earthquake precursory phenomena: evidence in the Kobe earthquake, Japan . J. Phys. I. France , 6 , 1391 – 1402 .
Jordan T.H. , 1997 . Is the study of earthquakes a basic science ?, Seism. Res. Lett. , 68 , 259 – 261 .
Kagan Y.Y. , 1982 . Stochastic model of earthquake fault geometry , Geophys. J. R. astr. Soc. , 71 , 659 – 691 .
Kagan Y.Y. , 1991 . Likelihood analysis of earthquake catalogue , Geophys. J. Int. , 106 , 135 – 148 .
Kagan Y.Y. , 1994 . Observational evidence for earthquakes as a nonlinear dynamic process , Physica D , 77 , 160 – 192 .
Kagan Y.Y. , 1996 . Comment on ‘The Gutenberg-Richter or characteristic earthquake distribution, which is it?’ by S. G. Wesnousky , Bull. seism. Soc. Am. , 86 , 274 – 285 .
Kagan Y.Y. , 1996 . VAN earthquake predictions—an attempt at statistical evaluation , Geophys. Res. Lett. , 23 , 1315 – 1318 .
Kagan Y.Y. , 1997 . Seismic moment-frequency relation for shallow earthquakes: regional comparison , J. geophys. Res. , 102 , 2835 – 2852 .
Kagan Y.Y. , 1997 . Statistical aspects of Parkfield earthquake sequence and Parkfield prediction experiment . Tectonophysics , 270 , 207 – 219 .
Kagan Y.Y. Jackson D.D. , 1991 . Seismic gap hypothesis: ten years after , J. geophys. Res. , 96 , 21419 – 21431 .
Kagan Y.Y. Jackson D.D. , 1994 . Long-term probabilistic fore-casting of earthquakes , J. geophys. Res. , 99 , 13685 – 13700 .
Kagan Y.Y. Jackson D.D. , 1994 . Earthquake prediction: A sorrowful tale , Eos. Trans. Am. geophys. Un. , Suppl. , 75 (25), 57 (abstract).
Kagan Y.Y. Jackson D.D. , 1995 . New seismic gap hypothesis: five years after , J. geophys. Res. , 100 , 3943 – 3959 .
Kagan Y.Y. Jackson D.D. , 1996 . Statistical tests of VAN earthquake predictions: comments and reflections , Geophys. Res. Lett. , 23 , 1433 – 1436 .
Kagan Y.Y. Knopoff L. , 1977 . Earthquake risk prediction as a stochastic process , Phys. Earth planet. Inter. , 14 , 97 – 108 .
Kagan Y.Y. Knopoff L. , 1987 . Statistical short-term earthquake prediction , Science , 236 , 1563 – 1567 .
Kagan Y.Y. Vere-Jones D. , 1996 . Problems in the modelling and statistical analysis of earthquakes , in Lecture Notes in Statistics , 114 , pp. 398 – 425 , eds Heyde C.C. Prohorov Yu.V. Pyke R. Racher S.T. , Springer , New York, NY.
Keilis-Borok V.I. Kossobokov V.G. 1990 . Premonitory activation of an earthquake flow: algorithm M 8 , Phys. Earth planet. Inter. , 61 , 73 – 83 .
Keilis-Borok V.I. Knopoff L. Rotwain I.M. Allen C.R. , 1988 . Intermediate-term prediction of occurrence times of strong earthquakes , Nature , 335 , 690 – 694 .
Kelleher J.A. Sykes L.R. Oliver J. , 1973 . Possible criteria for predicting earthquake locations and their applications to major plate boundaries of the Pacific and Caribbean , J. geophys. Res. , 78 , 2547 – 2585 .
King G.C.P. Stein R.S. Lin J. , 1994 . Static stress changes and the triggering of earthquakes , Bull. seism. Soc. Am. , 84 , 935 – 953 .
Knopoff L. , 1996 . Earthquake prediction—the scientific challenge , Proc. Nat. Acad. Sci. USA , 93 , 3719 – 3720 .
Knopoff L. Aki K. Allen C.R. Rice J.R. Sykes L. , eds., 1996 . Earthquake Prediction: The Scientific Challenge , Colloquium Proceedings , Proc. Nat. Acad. Sci. USA , 93 , 3719 – 3837 .
Kossobokov V.G. Healy J.H. Dewey J.W. 1997 . Testing an earthquake prediction algorithm , Pure appl. Geophys. , 149 , 219 – 232 .
Lighthill J. , ed., 1996 . Critical Review of VAN, Earthquake Prediction from Seismic Electrical Signals , World Scientific , Singapore.
Lomnitz C. , 1994 . Fundamentals of Earthquake Prediction , Wiley , New York, NY.
Ma Z. Fu Z. Zhang Y. Wang C. Zhang G. Liu D. , eds., 1990 . Earthquake Prediction: Nine Major Earthquakes in China (1966–1976) , Springer-Verlag , New York, NY.
Main I.G. , 1996 . Statistical physics, Seismogenesis, and seismic hazard , Rev. Geophys. , 34 , 433 – 462 .
Malone S. , 1996 . ‘ Near’ realtime seismology , Seism. Res. Lett. , 67 , 52 – 54 .
Mandelbrot B.B. , 1983 . The Fractal Geometry of Nature , 2nd edn, W. H. Freeman , San Francisco, CA.
Marder M. , 1996 . Energetic developments in fracture , Nature , 381 , 275 – 276 .
Marder M. Fineberg J. , 1996 . How things break , Physics Today , 49 ( 9 ), 24 – 29 .
McCann W.R. Nishenko S.P. Sykes L.R. Krause J. , 1979 . Seismic gap and plate tectonics: seismic potential for major boundaries , Pure appl. Geophys. , 117 , 1082 – 1147 .
Mogi K. , 1995 . Earthquake prediction research in Japan , J. Phys. Earth , 43 , 533 – 561 .
Molchan G.M. , 1997 . Earthquake prediction as a decision-making problem , Pure appl. Geophys. , 149 , 233 – 247 .
Molchan G.M. Kagan Y.Y. , 1992 . Earthquake prediction and its optimization , J. geophys. Res. , 97 , 4823 – 4838 .
Molchan G.M. Rotwain I.M. , 1985 . Statistical analysis of long-range precursors of strong earthquakes , in Computational Seismology , 15 , pp. 56 – 70 , eds Keilis-Borok V.I. Levshin A.L. , Allerton Press , New York, NY.
Molchan G.M. Dmitrieva O.E. Rotwain I.M. Dewey J. , 1990 . Statistical analysis of the results of earthquake prediction, based on bursts of aftershocks , Phys. Earth planet. Inter. , 61 , 128 – 139 .
Morowitz H.J. , 1996 . Complexity and epistemology , in Boundaries and Barriers: on the Limits to Scientific Knowledge , pp. 188 – 198 , eds Casti J.L. Karlqvist A. , Addison-Wesley , Reading, MA.
Mulargia F. , 1997 . Retrospective validation of time association , Geophys. J. Int. , 131 , 500 – 504 (this issue).
Mulargia F. Gasperini P. 1992 . Evaluating the statistical validity beyond chance of VAN earthquake precursors , Geophys. J. Int. , 111 , 32 – 44 .
Murphy A.H. , 1993 . What is a good forecast—an essay on the nature of goodness in weather forecasting , Weather Forecasting , 8 , 281 – 293 .
Murphy A.H. , 1996 . General decompositions of MSE-based skill scores—measures of some basic aspects of forecast quality , Mon. Weather Rev. , 124 , 2353 – 2369 .
Nishenko S.P. , 1989 . Earthquakes: hazards and predictions , in The Encyclopedia of Solid Earth Geophysics , pp. 260 – 268 , ed. James D.E. , Van Nostrand Reinhold Co. , New York, NY.
Nishenko S.P. , 1991 . Circum-Pacific seismic potential: 1989–1999 . Pure appl. Geophys. , 135 , 169 – 259 .
Nishenko S.P. Sykes L.R. , 1993 . Comment on ‘Seismic gap hypothesis: Ten years after’ by Y. Y. Kagan & D. D. Jackson , J. geophys. Res. , 98 , 9909 – 9916 .
NRC (National Research Council) Panel , 1991 . Real-time Earthquake Monitoring: Early Warning and Rapid Response , National Academy Press , Washington, DC.
Ogata Y. , 1988 . Statistical models for earthquake occurrence and residual analysis for point processes , J. Am. stat. Assoc. , 83 , 9 – 27 .
Ogata Y. , 1998 . Space-time point-process models for earthquake occurrences , Ann. Inst. star. Math. , in press.
Ogata Y. Utsu T. Katsura K. , 1996 . Statistical discrimination of foreshocks from other earthquake clusters , Geophys. J. Int. , 127 , 17 – 30 .
Olsen K.B. Archuleta R.J. Matarese J.R. , 1995 . 3-Dimensional simulation of a magnitude-7.75 earthquake on the San-Andreas fault , Science , 270 , 1628 – 1632 .
Omori F. , 1894 . On the after-shocks of earthquakes , J. College Sci., Imp. Univ. Tokyo , 7 , 111 – 200 .
Popper K.R. , 1980 . Logic of Scientific Discovery , Hutchinson , London.
Reasenberg P.A. Jones L.M. , 1989 . Earthquake hazard after a mainshock in California , Science , 243 , 1173 – 1176 .
Reasenberg P.A. Matthews M.V. , 1988 . Precursory seismic quiescence: a preliminary assessment of the hypothesis , Pure appl. Geophys. , 126 , 373 – 406 .
Reid H.F. , 1910 . Elastic rebound theory , Univ. Calif. Publ. Bull. Dept. Geol. Sci. , 6 , 92 – 120 .
Rhoades D.A. Evison F.F. , 1979 . Long-range earthquake forecasting based on a single predictor , Geophys. J. R. astr. Soc. , 59 , 43 – 56 .
Rhoades D.A. Evison F.F. , 1989 . On the reliability of precursors , Phys. Earth planet. Inter. , 58 , 137 – 140 .
Rhoades D.A. Evison F.F. , 1993 . Long-range earthquake forecasting based on a single predictor with clustering , Geophys. J. Int. , 113 , 371 – 381 .
Rhoades D.A. Evison F.F. , 1996 . The VAN earthquake predictions , Geophys. Res. Lett. , 23 , 1371 – 1374 .
Roeloffs E. Langbein J. , 1994 . The earthquake prediction experiment at Parkfield, California , Rev. Geophys. , 32 , 315 – 336 .
Ruelle D. , 1991 . Chance and Chaos , Princeton University Press , Princeton, NJ.
Savage J.C. , 1993 . The Parkfield prediction fallacy , Bull. seism. Soc. Am. , 83 , 1 – 6 .
Scholz C. , 1997 . Whatever happened to earthquake prediction ?, Geotimes , 42 , 16 – 19 .
Scholz C.H. , 1991 . Earthquakes and faulting: self-organized critical phenomena with a characteristic dimension , in Spontaneous Formation of Space-time Structures and Criticality , pp. 41 – 56 , eds Riste T. Sherrington D. , Kluwer Academic , Dordrecht.
Scrivner C.W. Helmberger D.V. , 1995 . Preliminary work on an early warning and rapid response program for moderate earthquakes , Bull. seism. Soc. Am. , 85 , 1257 – 1265 .
Shimazaki K. Nakata T. , 1980 . Time-predictable recurrence model for large earthquakes , Geophys. Res. Lett. , 7 , 279 – 282 .
Sornette D. Sammis C.G. , 1995 . Complex critical exponents from renormalization group theory of earthquakes: implications for earthquake predictions , J. Phys. I. France , 5 , 607 – 619 .
Stark P.B. , 1996 . A few statistical considerations for ascribing statistical significance to earthquake predictions , Geophys. Res. Lett. , 23 , 1399 – 1402 .
Stark P.B. , 1997 . Earthquake prediction: the null hypothesis , Geophys. J. Int. , 131 , 495 – 499 (this issue).
Svozil K. , 1995 . Set theory and physics , Foundations Phys. , 25 , 1514 – 1560 .
Turcotte D.L. , 1991 . Earthquake prediction , Ann. Rev. Earth planet. Sci. , 19 , 263 – 281 .
US Geological Survey , 1996 . Preliminary Determination of Epicentres (PDE), Monthly Listings , US Dept Interior, National Earthquake Information Centre , Denver, CO.
Utsu T. Ogata Y. Matsu'ura R.S. , 1995 . The centenary of the Omori formula for a decay law of aftershock activity , J. Phys. Earth. 43 , 1 – 33 .
Varnes D.J. Bufe C.G. , 1996 . The cyclic and fractal seismic series preceding an m b 4.8 earthquake on 1980 February 14 near the Virgin Islands , Geophys. J. Int. , 124 , 149 – 158 .
Varotsos P. Lazaridou M. , 1996 . Reply to ‘Difficulty of statistical evaluation of an earthquake prediction method’, by Utada , Geophys. Res. Lett. , 23 , 1395 – 1398 .
Varotsos P. Alexopoulos K. Nomicos K. , 1981 . Seven-hour precursors to earthquakes determined from telluric currents . Praktika Acad. Athens , 56 , 417 – 433 .
Varotsos P. Eftaxias K. Vallianatos F. Lazaridou M. , 1996 . Basic principles for evaluating and earthquake prediction method . Geophys. Res. Lett. , 23 , 1295 – 1298 .
Varotsos P. Lazaridou M. Eftaxias K. Antonopoulos G. Makris J. Kopanas J. , 1996 . Short-term earthquake prediction in Greece by seismic electric signals , in A Critical Review of VAN . pp. 29 – 76 , ed. Lighthill J. , World Scientific , Singapore.
Vere-Jones D. , 1978 . Earthquake prediction—a statistician's view . J. Phys. Earth , 26 , 129 – 146 .
Vere-Jones D. , 1995 . Forecasting earthquakes and earthquake risk . Int. J. Forecasting , 11 , 503 – 538 .
Vilenkin N.Y. , 1995 . In Search of Infinity , Birkhauser , Boston, MA.
Wallace R.E. Davis J.F. McNally K.C. , 1984 . Terms for expressing earthquake potential, prediction and probability , Bull. seism. Soc. Am. , 74 , 1819 – 1825 .
Weimer S. Wyss M. , 1994 . Seismic quiescence before the Landers (M = 7.5) and Big Bear (M = 6.5) 1992 earthquakes , Bull. seism. Soc. Am. , 84 , 900 – 916 .
White L. Tursky B. Schwartz G.E. , eds., 1985 . Placebo: Theory. Research and Mechanisms , Guilford Press , New York, NY.
Working Group on the probabilities of future large earthquakes in southern California , 1995 . Seismic hazards in southern California: Probable earthquakes, 1994–2024 , Bull. seism. Soc. Am. , 85 , 379 – 439 .
Wyss M. ed., 1991 . Evaluation of Proposed Earthquake Precursors , American Geophysical Union , Washington, DC.
Wyss M. Dmowska R. , eds., 1997 . Earthquake Prediction—State of the Art , Pure appl. Geophys. , 149 , 1 – 264 .
Month: | Total Views: |
---|---|
January 2017 | 10 |
February 2017 | 25 |
March 2017 | 28 |
April 2017 | 15 |
May 2017 | 19 |
June 2017 | 8 |
July 2017 | 10 |
August 2017 | 21 |
September 2017 | 28 |
October 2017 | 155 |
November 2017 | 52 |
December 2017 | 459 |
January 2018 | 373 |
February 2018 | 254 |
March 2018 | 345 |
April 2018 | 398 |
May 2018 | 37 |
June 2018 | 15 |
July 2018 | 18 |
August 2018 | 12 |
September 2018 | 9 |
October 2018 | 15 |
November 2018 | 24 |
December 2018 | 12 |
January 2019 | 8 |
February 2019 | 20 |
March 2019 | 34 |
April 2019 | 19 |
May 2019 | 19 |
June 2019 | 21 |
July 2019 | 13 |
August 2019 | 20 |
September 2019 | 10 |
October 2019 | 20 |
November 2019 | 28 |
December 2019 | 20 |
January 2020 | 22 |
February 2020 | 25 |
March 2020 | 13 |
April 2020 | 21 |
May 2020 | 39 |
June 2020 | 67 |
July 2020 | 51 |
August 2020 | 32 |
September 2020 | 86 |
October 2020 | 104 |
November 2020 | 113 |
December 2020 | 96 |
January 2021 | 69 |
February 2021 | 101 |
March 2021 | 100 |
April 2021 | 79 |
May 2021 | 99 |
June 2021 | 46 |
July 2021 | 54 |
August 2021 | 54 |
September 2021 | 146 |
October 2021 | 101 |
November 2021 | 74 |
December 2021 | 61 |
January 2022 | 47 |
February 2022 | 61 |
March 2022 | 57 |
April 2022 | 45 |
May 2022 | 38 |
June 2022 | 25 |
July 2022 | 19 |
August 2022 | 19 |
September 2022 | 41 |
October 2022 | 49 |
November 2022 | 14 |
December 2022 | 26 |
January 2023 | 16 |
February 2023 | 23 |
March 2023 | 30 |
April 2023 | 19 |
May 2023 | 15 |
June 2023 | 8 |
July 2023 | 11 |
August 2023 | 13 |
September 2023 | 20 |
October 2023 | 20 |
November 2023 | 22 |
December 2023 | 22 |
January 2024 | 20 |
February 2024 | 22 |
March 2024 | 35 |
April 2024 | 33 |
May 2024 | 26 |
June 2024 | 22 |
July 2024 | 16 |
August 2024 | 11 |
Email alerts
Astrophysics data system, citing articles via.
- Recommend to your Library
- Advertising and Corporate Services
- Journals Career Network
Affiliations
- Online ISSN 1365-246X
- Copyright © 2024 The Royal Astronomical Society
- About Oxford Academic
- Publish journals with us
- University press partners
- What we publish
- New features
- Open access
- Institutional account management
- Rights and permissions
- Get help with access
- Accessibility
- Advertising
- Media enquiries
- Oxford University Press
- Oxford Languages
- University of Oxford
Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide
- Copyright © 2024 Oxford University Press
- Cookie settings
- Cookie policy
- Privacy policy
- Legal notice
This Feature Is Available To Subscribers Only
Sign In or Create an Account
This PDF is available to Subscribers Only
For full access to this pdf, sign in to an existing account, or purchase an annual subscription.
- A-Z Publications
Annual Review of Earth and Planetary Sciences
Volume 45, 2017, review article, statistics of earthquake activity: models and methods for earthquake predictability studies.
- Yosihiko Ogata 1
- View Affiliations Hide Affiliations Affiliations: Institute of Statistical Mathematics, Tokyo 190-8562, Japan; email: [email protected]
- Vol. 45:497-527 (Volume publication date August 2017) https://doi.org/10.1146/annurev-earth-063016-015918
- First published as a Review in Advance on June 28, 2017
- © Annual Reviews
Statistical methods and various models in time-space-magnitude parameter space of earthquakes are being developed to analyze seismic activity based on earthquake hypocenter catalogs that are routinely accumulated. Considering complex geophysical environments and uncertainties, we seek proper stochastic modeling that depends on the history of earthquake occurrences and relevant geophysical information for describing and forecasting earthquake activity. Also, we need empirical Bayesian models with many parameters in order to describe nonstationary or nonhomogeneous seismic activity. This review is concerned with earthquake predictability research aimed at realizing practical operational forecasting. In particular, uncertainty lies in identifying whether abnormal phenomena are precursors to large earthquakes. The predictability of such models can be examined by certain statistical criteria.
Article metrics loading...
Full text loading...
Literature Cited
- Agnew DC , Jones LM . 1991 . Prediction probabilities from foreshocks. J. Geophys. Res. 96 : B7 11959– 71 [Google Scholar]
- Akaike H . 1974 . A new look at the statistical model identification. IEEE Trans. Autom. Control 19 : 716– 23 [Google Scholar]
- Akaike H . 1978a . A new look at the Bayes procedure. Biometrika 65 : 53– 59 [Google Scholar]
- Akaike H . 1978b . On the likelihood of a time series model. Statistician 27 : 217– 35 [Google Scholar]
- Akaike H . 1980 . Likelihood and Bayes procedure. Bayesisn Statistics JM Bernard, MH De Groot, DU Lindley, AFM Smith 143– 66 (discussion 185–203) Valencia, Spain: Univ. Press [Google Scholar]
- Akaike H . 1985 . Prediction and entropy. A Celebration of Statistics : The ISI Centenary Volume AC Atkinson, SE Fienberg 1– 24 New York: Springer-Verlag [Google Scholar]
- Aki K . 1956 . A review on statistical seismology. Zisin II [J. Seismol. Soc. Jpn.] 8 : 205– 28 [Google Scholar]
- Aki K . 1965 . Maximum likelihood estimate of b in the formula log N = a − bM and its confidence limits. Bull. Earthq. Res. Inst. Univ. Tokyo 43 : 237– 39 [Google Scholar]
- Aki K . 1981 . A probabilistic synthesis of precursory phenomena. Earthquake Prediction , ed. DW Simpson, PG Richards, 566– 74 Washington, DC: Am. Geophys.Union
- Bansal AR , Ogata Y . 2013 . A non-stationary epidemic type aftershock sequence model for seismicity prior to the December 26, 2004 M9.1 Sumatra-Andaman Islands mega-earthquake. J. Geophys. Res. Solid Earth 118 : 616– 29 [Google Scholar]
- Bebbington M , Harte D . 2003 . The linked stress release model for spatio-temporal seismicity: formulations, procedure and applications. Geophys. J. Int. 154 : 925– 46 [Google Scholar]
- Boltzmann L . 1878 . Weitere Bemerkungen uber einige Probleme der mechanischen Warmetheorie. Wiener Ber 78 : 7– 46 [Google Scholar]
- Cao T , Aki K . 1983 . Assigning probability gain for precursors of four large Chinese earthquakes. J. Geophys. Res. 88 : B3 2185– 90 [Google Scholar]
- Console R , Murru M , Alessandrini B . 1993 . Foreshock statistics and their possible relationship to earthquake prediction in the Italian region. Bull. Seismol. Soc. Am. 83 : 1248– 63 [Google Scholar]
- Cox DR , Lewis PAW . 1966 . The Statistical Analysis of Series of Events London: Methuen [Google Scholar]
- Cox DR , Snell EJ . 1989 . Analysis of Binary Data London: Chapman & Hall/CRC. , 2nd ed.. [Google Scholar]
- Daley DJ , Vere-Jones D . 2003 . An Introduction to the Theory of Point Processes . Vol 1: Elementary Theory and Methods New York: Springer, 2nd ed.. [Google Scholar]
- De Natale G , Musmeci F , Zollo A . 1988 . A linear intensity model to investigate the causal relation between Calabrian and North-Aegean earthquake sequences. Geophys. J. 95 : 285– 93 [Google Scholar]
- Delaunay B . 1934 . Sur la sphère vide. Bull. Acad. Sci.URSS 6 : 793– 800 [Google Scholar]
- ERC (Earthq. Res. Comm.). 2001 . Regarding methods for evaluating long-term probability of earthquake occurrence Rep., Hqrs. Earthq. Res. Promot Tokyo: http://www.jishin.go.jp/main/choukihyoka/01b/chouki020326.pdf [Google Scholar]
- Evison FF . 1977 . The precursory earthquake swarm. Phys. Earth Planet. Inter. 15 : 19– 23 [Google Scholar]
- Frohlich C , Davis SD . 1990 . Single-link cluster analysis as a method to evaluate spatial and temporal properties of earthquake catalogs. Geophys. J. Int. 100 : 19– 32 [Google Scholar]
- Gerstenberger M , Wiemer S , Jones R . 2005 . Real-time forecasts of tomorrow's earthquakes in California. Nature 435 : 328– 31 [Google Scholar]
- Good IJ , Gaskins RA . 1971 . Nonparametric roughness penalties for probability densities. Biometrika 58 : 255– 77 [Google Scholar]
- Gutenberg R , Richter CF . 1944 . Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34 : 185– 88 [Google Scholar]
- Hawkes AG . 1971 . Point spectra of some mutually exciting point processes. J. R. Stat. Soc. B 33 : 438– 43 [Google Scholar]
- Hawkes AG , Adamopoulos L . 1973 . Cluster models for earthquakes—regional comparisons. Bull. Int. Stat. Inst. 45 : Book 3 454– 61 [Google Scholar]
- Hawkes AG , Oakes DA . 1974 . A cluster process representation of a self-exciting process. J. Appl. Probab. 11 : 493– 503 [Google Scholar]
- Helmstetter A , Sornette D . 2003 . Foreshocks explained by cascades of triggered seismicity. J. Geophys. Res. 108 : B10 2457 [Google Scholar]
- Inouye W . 1965 . On the seismicity in the epicentral region and its neighborhood before the Niigata earthquake. Kenshin-jiho [Q. J. Seismol.] 29 : 139– 144 [Google Scholar]
- Iwata T . 2015 . Earthquake forecasting based on the correlation between earth tides and earthquake occurrences. Proc. Inst. Stat. Math. 63 : 1 129– 44 [Google Scholar]
- Iwata T , Katao H . 2006a . Correlation between the phase of the moon and the occurrences of microearthquakes in the Tamba region by a modified conditional intensity model Presented at Seismol. Soc. Jpn. Fall Meet., Oct 31– Nov 2 Nagoya, Jpn., Abstr. P212 [Google Scholar]
- Iwata T , Katao H . 2006b . Correlation between the phase of the moon and the occurrences of microearthquakes in the Tamba region through point-process modeling. Geophys. Res. Lett. 33 : L07302 [Google Scholar]
- Jones LM . 1985 . Foreshocks and time-dependent earthquake hazard assessment in southern California. Bull. Seismol. Soc. Am. 75 : 1669– 79 [Google Scholar]
- Jones LM . 1994 . Foreshocks, aftershocks and earthquake probabilities: accounting for Landers earthquakes. Bull. Seismol. Soc. Am. 85 : 892– 99 [Google Scholar]
- Jones LM , Molnar P . 1979 . Some characteristics of foreshocks and their possible relationship to earthquake prediction and premonitory slip on faults. J. Geophys. Res. 84 : B7 3596– 608 [Google Scholar]
- Jordan TH , Chen YT , Gasparini P , Madariaga R , Marzocchi W . et al. 2011 . Operational earthquake forecasting: state of knowledge and guidelines for utilization. Ann. Geophys. 54 : 4 315– 91 [Google Scholar]
- Kagan YY . 1991 . Likelihood analysis of earthquake catalogues. Geophys. J. Int. 106 : 135– 48 [Google Scholar]
- Kagan YY . 2014 . Earthquakes : Models, Statistics, Testable Forecasts Chichester, UK: Wiley [Google Scholar]
- Kagan YY , Jackson D . 1995 . New seismic gap hypothesis—5 years after. J. Geophys. Res. 100 : B3 3943– 59 [Google Scholar]
- Kagan YY , Knopoff L . 1987 . Random stress and earthquake statistics: time dependence. Geophys. J. R. Astron. Soc. 88 : 723– 31 [Google Scholar]
- Karr AN . 1986 . Point Processes and Their Statistical Inference New York: Marcel Dekker [Google Scholar]
- Kendall DG . 1949 . Stochastic processes and population growth. J. R. Stat. Soc. B 11 : 230– 64 [Google Scholar]
- Kumazawa T , Ogata Y . 2013 . Quantitative description of induced seismic activity before and after the 2011 Tohoku-Oki earthquake by non-stationary ETAS models. J. Geophys. Res. Solid Earth 118 : 6165– 82 [Google Scholar]
- Kumazawa T , Ogata Y , Kimura K , Maeda K , Kobayashi A . 2016a . Background rates of swarm earthquakes that are synchronized with volumetric strain changes. Earth Planet. Sci. Lett. 442 : 51– 60 [Google Scholar]
- Kumazawa T , Ogata Y , Toda S . 2010 . Precursory seismic anomalies and transient crustal deformation prior to the 2008 M w = 6.9 Iwate-Miyagi Nairiku, Japan, earthquake. J. Geophys. Res. 115 : B10312 [Google Scholar]
- Kumazawa T , Ogata Y , Tsuruoka H . 2016b . Statistical monitoring of seismicity in Kyushu District before the occurrence of the 2016 Kumamoto earthquakes of M6.5 and M7.3. Rep. Coord. Comm. Earthq. Predic. 96 : 12-8 412– 23 [Google Scholar]
- Kutoyants YU . 1984 . Parameter Estimation for Stochastic Processes Berlin: Heldermann [Google Scholar]
- Laplace PS . 1774 (1986) . Memoir on the probability of causes of events. 6 : transl. SM Stigler Stat. Sci . 1 : 19 364– 78 [Google Scholar]
- Lewis PAW , Shedler GS . 1979 . Simulation of nonhomogeneous Poisson processes by thinning. Nav. Res. Logist. Q. 26 : 3 403– 13 [Google Scholar]
- Li M , Vere-Jones D . 1997 . Application of M8 and Lin-Lin algorithms to New Zealand earthquake data. N. Z. J. Geol. Geophys. 40 : 77– 89 [Google Scholar]
- Lomnitz C , Hax A . 1966 . Clustering in aftershock sequences. The Earth Beneath the Continents JS Steinhart, TJ Smith 502– 8 Washington, DC: Am. Geophys. Union [Google Scholar]
- Lomnitz C , Nava FA . 1983 . The predictive value of seismic gaps. Bull. Seismol. Soc. Am. 73 : 1815– 24 [Google Scholar]
- Lu C , Harte D , Bebbington M . 1999 . A linked stress release for historical Japan earthquakes: coupling among major seismic regions. Earth Planets Space 51 : 907– 16 [Google Scholar]
- Maeda K . 1993 . An empirical alarm criterion based on immediate foreshocks: a case study for the Izu region. Zisin II [J. Seismol. Soc. Jpn.] 45 : 373– 83 [Google Scholar]
- Mantovani E , Albarello D , Mucciarelli M . 1986 . Seimic activity in North Aegean region as middle-term precursor of Calabrian earthquakes. Phys. Earth Planet. Int. 44 : 264– 73 [Google Scholar]
- Matsumura K . 1986 . On regional characteristics of seasonal variation of shallow earthquake activities in the world. Bull. Disaster Prev. Res. Inst. Kyoto Univ. 36 : 43– 98 [Google Scholar]
- Matthews MV , Ellsworth WL , Reasenberg PA . 2002 . A Brownian model for recurrent earthquakes. Bull. Seismol. Soc. Am. 92 : 2233– 50 [Google Scholar]
- Mogi K . 1962 . Magnitude frequency relations for elastic shocks accompanying fractures of various materials and some related problems in earthquakes. Bull. Earthq. Res. Inst. Univ. Tokyo 40 : 831– 53 [Google Scholar]
- Mogi K . 1963 . Some discussions on aftershocks, foreshocks, and earthquake swarms—the fracture of a semi-infinite body caused by an inner stress origin and its relation to the earthquake phenomena. Bull. Earthq. Res. Inst. Univ. Tokyo 41 : 615– 58 [Google Scholar]
- Mogi K . 1973 . Relationship between shallow and deep seismicity in the western Pacific region. Tectonophysics 17 : 1– 22 [Google Scholar]
- Musmeci F , Vere-Jones D . 1992 . A space-time clustering model for historical earthquakes. Ann. Inst. Stat. Math. 44 : 1– 11 [Google Scholar]
- Nadeau RM , McEvilly TV . 1999 . Fault slip rates at depth from recurrence intervals of repeating microearthquakes. Science 285 : 718– 21 [Google Scholar]
- Nanjo KZ , Tsuruoka H , Yokoi S , Ogata Y , Falcone G . et al. 2012 . Predictability study on the aftershock sequence following the 2011 Tohoku-Oki, Japan, earthquake: first results. Geophys. J. Int. 191 : 2 653– 58 [Google Scholar]
- Nishizawa O , Lei X , Nagao T . 1994 . Hazard function analysis of seismo-electric signals in Greece. Electromagnetic Phenomena Related to Earthquake Prediction M Hayakawa, Y Fujinawa 459– 74 Tokyo: Terra [Google Scholar]
- Nomura S . 2015 . Real-time classifier of foreshocks using probability density ratio estimation Presented at Jpn. Geosci. Union Meet., May 24, Chiba City, Jpn., Abstr. SSS01-01 [Google Scholar]
- Nomura S , Ogata Y . 2015a . Foreshock discrimination with probability density ratio estimation and its implementation for short-term forecast Presented at Seismol Soc. Jpn. Fall Meet. Oct. 27 Kobe City, Jpn. [Google Scholar]
- Nomura S , Ogata Y . 2015b . Spatial distribution of the coefficient of variation for the paleo-earthquakes in Japan Presented at AGU Fall Meet., Dec. 15 San Francisco: Abstr. S21D-05 [Google Scholar]
- Nomura S , Ogata Y , Komaki F , Toda S . 2011 . Bayesian forecasting of the recurrent earthquakes and its predictive performance for a small sample size. J. Geophys. Res. 116 : B04315 [Google Scholar]
- Nomura S , Ogata Y , Nadeau RM . 2014 . Space-time model for repeating earthquakes and analysis of recurrence intervals on the San Andreas Fault near Parkfield, California. J. Geophys. Res. Solid Earth 119 : 9 7092– 122 [Google Scholar]
- Nomura S , Ogata Y , Uchida N , Matu'ura M . 2016 . Spatiotemporal variations of interplate slip rates in northeast Japan inverted from recurrence intervals of repeating earthquakes. Geophys. J. Int. 208 : 468– 81 [Google Scholar]
- Ogata Y . 1978 . The asymptotic behavior of maximum likelihood estimator of stationary point processes. Ann. Inst. Stat. Math. 30 : 243– 61 [Google Scholar]
- Ogata Y . 1981 . On Lewis’ simulation method for point processes. IEEE Trans. Inf. Theory 27 : 23– 31 [Google Scholar]
- Ogata Y . 1983a . Estimation of the parameters in the modified Omori formula for aftershock frequencies by the maximum likelihood procedure. J. Phys. Earth. 31 : 115– 24 [Google Scholar]
- Ogata Y . 1983b . Likelihood analysis of point processes and its applications to seismological data. Bull. Int. Stat. Inst. 50 : 2 943– 61 [Google Scholar]
- Ogata Y . 1988 . Statistical models for earthquake occurrences and residual analysis for point processes. J. Am. Stat. Assoc. 83 : 9– 27 [Google Scholar]
- Ogata Y . 1989 . Statistical model for standard seismicity and detection of anomalies by residual analysis. Tectonophysics 169 : 159– 74 [Google Scholar]
- Ogata Y . 1991 . Space and time variations of detection rate and the magnitude frequency of earthquakes. Tokei Suri [ Proc. Inst. Stat. Math. ] 39 : 2 245– 56 [Google Scholar]
- Ogata Y . 1992 . Detection of precursory relative quiescence before great earthquakes through a statistical model. J. Geophys. Res. 97 : B13 19845– 71 [Google Scholar]
- Ogata Y . 1995 . Evaluation of probability forecasts of events. Int. J. Forecast. 11 : 539– 41 [Google Scholar]
- Ogata Y . 1998a . Quiescence relative to the ETAS model. Zisin II [J. Seismol. Soc. Jpn.] 50 : 115– 27 [Google Scholar]
- Ogata Y . 1998b . Space-time point-process models for earthquake occurrences. Ann. Inst. Stat. Math. 50 : 379– 402 [Google Scholar]
- Ogata Y . 1999a . Seismicity analysis through point-process modeling—a review. Pure Appl. Geophys. 155 : 471– 507 [Google Scholar]
- Ogata Y . 1999b . Estimating the hazard of rupture using uncertain occurrence times of paleoearthquakes. J. Geophys. Res. 104 : B8 17995– 8014 [Google Scholar]
- Ogata Y . 2001a . Exploratory analysis of earthquake clusters by likelihood-based trigger models. J. Appl. Probab. 38 : A 202– 12 [Google Scholar]
- Ogata Y . 2001b . Increased probability of large earthquakes near aftershock regions with relative quiescence. J. Geophys. Res. 106 : B5 8729– 44 [Google Scholar]
- Ogata Y . 2002 . Slip-size-dependent renewal processes and Bayesian inferences for uncertainties. J. Geophys. Res. 107 : B11 2268 [Google Scholar]
- Ogata Y . 2004 . Space-time model for regional seismicity and detection of crustal stress changes. J. Geophys. Res. 109 : B3 B03308 [Google Scholar]
- Ogata Y . 2006a . Monitoring of anomaly in the aftershock sequence of the 2005 earthquake of M7.0 off coast of the western Fukuoka, Japan, by the ETAS model. Geophys. Res. Lett. 33 : L01303 [Google Scholar]
- Ogata Y . 2006b . Statistical Analysis of Seismicity—Updated Version (SASeis2006) Comput. Sci. Monogr. 33 Tokyo: Inst. Stat. Math http://www.ism.ac.jp/∼ogata/Ssg/ssg_softwaresE.html [Google Scholar]
- Ogata Y . 2007 . Seismicity and geodetic anomalies in a wide area preceding the Niigata-Ken-Chuetsu earthquake of 23 October 2004, central Japan. J. Geophys. Res. 112 : B10301 [Google Scholar]
- Ogata Y . 2010a . Anomalies of seismic activity and transient crustal deformations preceding the 2005 M7.0 earthquake west of Fukuoka. Pure Appl. Geophys. 167 : 8–9 1115– 27 [Google Scholar]
- Ogata Y . 2010b . Space-time heterogeneity in aftershock activity. Geophys. J. Int. 181 : 3 1575– 92 [Google Scholar]
- Ogata Y . 2011a . Pre-seismic anomalies in seismicity and crustal deformation: case studies of the 2007 Noto Hanto earthquake of M6.9 and the 2007 Chuetsu-oki earthquake of M6.8 after the 2004 Chuetsu earthquake of M6.8. Geophys. J. Int. 186 : 331– 48 [Google Scholar]
- Ogata Y . 2011b . Significant improvements of the space-time ETAS model for forecasting of accurate baseline seismicity. Earth Planets Space 63 : 217– 29 [Google Scholar]
- Ogata Y . 2017 . Forecasting of a large earthquake: an outlook of the research. Seismol. Res. Lett. 88 : 4 1117– 26 [Google Scholar]
- Ogata Y , Abe K . 1991 . Some statistical features of the long term variation of the global and regional seismic activity. Int. Stat. Rev. 59 : 139– 61 [Google Scholar]
- Ogata Y , Akaike H . 1982 . On linear intensity models for mixed doubly stochastic Poisson and self-exciting point process. J. R. Stat. Soc. B 44 : 102– 7 [Google Scholar]
- Ogata Y , Akaike H , Katsura K . 1982 . The application of linear intensity models to the investigation of causal relations between a point process and another stochastic process. Ann. Inst. Stat. Math. 34 : 373– 87 [Google Scholar]
- Ogata Y , Imoto M , Katsura K . 1991 . 3-D spatial variation of b -values of magnitude-frequency distribution beneath the Kanto District, Japan. Geophys. J. Int. 104 : 135– 46 [Google Scholar]
- Ogata Y , Jones LM , Toda S . 2003a . When and where the aftershock activity was depressed: contrasting decay patterns of the proximate large earthquakes in southern California. J. Geophys. Res 108 : B6 2318 [Google Scholar]
- Ogata Y , Katsura K . 1986 . Point process model with linearly parameterized intensity for the application to earthquake data. J. Appl. Probab. 23 : A 291– 310 [Google Scholar]
- Ogata Y , Katsura K . 1988 . Likelihood analysis of spatial inhomogeneity for marked point patterns. Ann. Inst. Stat. Math. 40 : 29– 39 [Google Scholar]
- Ogata Y , Katsura K . 1993 . Analysis of temporal and spatial heterogeneity of magnitude frequency distribution inferred from earthquake catalogues. Geophys. J. Int. 113 : 727– 38 [Google Scholar]
- Ogata Y , Katsura K . 2006 . Immediate and updated forecasting of aftershock hazard. Geophys. Res. Lett. 33 : 10 L10305 [Google Scholar]
- Ogata Y , Katsura K . 2012 . Prospective foreshock forecast experiment during the last 17 years. Geophys. J. Int. 191 : 3 1237– 44 [Google Scholar]
- Ogata Y , Katsura K . 2014 . Comparing foreshock characteristics and foreshock forecasting in observed and simulated earthquake catalogs. J. Geophys. Res . Solid Earth 119 : 11 8457– 77 [Google Scholar]
- Ogata Y , Katsura K , Falcone G , Nanjo KZ , Zhuang J . 2013 . Comprehensive and topical evaluations of earthquake forecasts in terms of number, time, space, and magnitude. Bull. Seismol. Soc. Am. 103 : 1692– 708 [Google Scholar]
- Ogata Y , Katsura K , Tanemura M . 2003b . Modelling heterogeneous space-time occurrences of earthquakes and its residual analysis. J. R. Stat. Soc. C 52 : 4 499– 509 [Google Scholar]
- Ogata Y , Kobayashi A , Mikami N , Murata Y , Katsura K . 1998 . Correction of earthquake location estimation in a small-seismic-array system. Bernoulli 4 : 167– 84 [Google Scholar]
- Ogata Y , Matsu'ura RS , Katsura K . 1993 . Fast likelihood computation of epidemic type aftershock-sequence model. Geophys. Res. Lett. 20 : 2143– 46 [Google Scholar]
- Ogata Y , Utsu T , Katsura K . 1995 . Statistical features of foreshocks in comparison with other earthquake clusters. Geophys. J. Int. 121 : 233– 54 [Google Scholar]
- Ogata Y , Utsu T , Katsura K . 1996 . Statistical discrimination of foreshocks from other earthquake clusters. Geophys. J. Int. 127 : 17– 30 [Google Scholar]
- Ogata Y , Zhuang J . 2001 . Statistical examination of anomalies for the precursor to earthquakes, and the multi-element prediction formula: hazard rate changes of strong earthquakes (M ≥ 4) around Beijing area based on the ultra-low frequency ground electric observation (1982–1997). Rep. Coord. Comm. Earthq. Predict. 66 : 562– 70 [Google Scholar]
- Ogata Y , Zhuang J . 2006 . Space-time ETAS models and an improved extension. Tectonophysics 413 : 13– 23 [Google Scholar]
- Omi T , Ogata Y , Hirata Y , Aihara K . 2013 . Forecasting large aftershocks within one day after the main shock. Sci. Rep. 3 : 2218 [Google Scholar]
- Omi T , Ogata Y , Hirata Y , Aihara K . 2014a . Estimating the ETAS model from an early aftershock sequence. Geophys. Res. Lett. 41 : 850– 57 [Google Scholar]
- Omi T , Ogata Y , Hirata Y , Aihara K . 2014b . Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches. J. Geophys. Res. Solid Earth 120 : 2561– 78 [Google Scholar]
- Omori F . 1894 . On the aftershocks of earthquake. J. Coll. Sci. Imp. Univ. Tokyo 7 : 111– 200 [Google Scholar]
- Papangelou F . 1972 . Integrability of expected increments of point processes and related random change of scale. Trans. Am. Math. Soc. 165 : 483– 506 [Google Scholar]
- Papazachos BC . 1974 . On certain aftershock and foreshock parameters in the area of Greece. Ann. Geophys. 27 : 497– 515 [Google Scholar]
- Parzen E , Tanabe K , Kitagawa G . 1998 . Selected Papers of Hirotugu Akaike Tokyo: Springer-Verlag [Google Scholar]
- Rathbun SL . 1993 . Modeling marked spatio-temporal point patterns. Bull. Int. Stat. Inst. 55 : 379– 96 [Google Scholar]
- Reasenberg PA , Jones LM . 1989 . Earthquake hazard after a main shock in California. Science 243 : 1173– 76 [Google Scholar]
- Rhoades DA , Evison FF . 2004 . Long-range earthquake forecasting with every earthquake a precursor according to scale. Pure Appl. Geophys. 161 : 1 47– 72 [Google Scholar]
- Ringdal F . 1975 . On the estimation of seismic detection thresholds. Bull. Seismol. Soc. Am. 65 : 1631– 42 [Google Scholar]
- Savage MK , de Polo DM . 1993 . Foreshock probabilities in the western Great-Basin eastern Sierra Nevada. Bull. Seismol. Soc. Am. 83 : 1910– 38 [Google Scholar]
- Schoenberg FP . 2004 . Testing separability in spatial-temporal marked point processes. Biometrics 60 : 471– 81 [Google Scholar]
- Scholz C . 1968 . The frequency-magnitude relation of microfracturing in rock and its relation to earthquakes. Bull. Seismol. Soc. Am. 58 : 1 399– 415 [Google Scholar]
- Schorlemmer D , Zechar J , Werner M , Field E , Jackson D . et al. 2010 . First results of the Regional Earthquake Likelihood Models experiment. Pure Appl. Geophys. 167 : 8–9 859– 76 [Google Scholar]
- Sekiya H . 1976 . The seismicity preceding earthquakes and its significance to earthquake prediction. Zisin II [J. Seismol. Soc. Jpn.] 29 : 299– 312 [Google Scholar]
- Smith WD . 1986 . Evidence for precursory changes in the frequency magnitude b -value. Geophys. J. Int. 86 : 815– 38 [Google Scholar]
- Smyth C , Mori J . 2011 . Statistical models for temporal variations of seismicity parameters to forecast seismicity rates in Japan. Earth Planets Space 63 : 231– 38 [Google Scholar]
- Suyehiro S . 1966 . Difference between aftershocks and foreshocks in the relationship of magnitude to frequency of occurrence for the great Chilean earthquake of 1960. Bull. Seismol. Soc. Am. 56 : 185– 200 [Google Scholar]
- Utsu T . 1961 . A statistical study on the occurrence of aftershocks. Geophys. Mag. 30 : 521– 605 [Google Scholar]
- Utsu T . 1965 . A method for determining the value of b in a formula log n = a − bM showing the magnitude-frequency relation for earthquakes. Geophys. Bull. Hokkaido Univ 13 99– 103 [Google Scholar]
- Utsu T . 1969 . Aftershocks and earthquake statistics (I): some parameters which characterize an aftershock sequence and their interaction. J. Fac. Sci. Hokkaido Univ. Ser 7, 3 : 129– 95 [Google Scholar]
- Utsu T . 1970 . Aftershocks and earthquake statistics (II): further investigation of aftershocks and other earthquake sequences based on a new classification of earthquake sequences. J. Fac. Sci. Hokkaido Univ. , Ser 7, 3 : 197– 266 [Google Scholar]
- Utsu T . 1971 . Aftershock and earthquake statistic (III): analyses of the distribution of earthquakes in magnitude, time and space with special consideration to clustering characteristics of earthquake occurrence (1). J. Fac. Sci. Hokkaido Univ. Ser 7, 3 : 379– 441 [Google Scholar]
- Utsu T . 1972 . Aftershocks and earthquake statistics (IV): analyses of the distribution of earthquakes in magnitude, time, and space with special consideration to clustering characteristics to earthquake occurrence (2). J. Fac. Sci. Hokkaido Univ. Ser 7, 3 : 379– 441 [Google Scholar]
- Utsu T . 1975 . Correlation between shallow earthquakes in Kwanto region and intermediate earthquakes in Hida region, central Japan. Zisin II [J. Seismol. Soc. Jpn.] 28 : 303– 11 [Google Scholar]
- Utsu T . 1977 . Probability in earthquake prediction. Zisin II [J. Seismol. Soc. Jpn.] 30 : 179– 85 [Google Scholar]
- Utsu T . 1978 . An investigation into the discrimination of foreshock sequences from earthquake swarms. Zisin II [J. Seismol. Soc. Jpn.] 31 : 129– 35 [Google Scholar]
- Utsu T . 1979 . Calculation of the probability of success of an earthquake prediction (In the case of Izu-Oshima-Kinkai earthquake of 1978). Rep. Coord. Comm. Earthq. Predict. 21 : 164– 66 [Google Scholar]
- Utsu T . 1984 . Estimation of parameters for recurrence models of earthquakes. Bull. Earthq. Res. Inst. Univ. Tokyo 59 : 53– 66 [Google Scholar]
- Utsu T . 1999a . Representation and analysis of the earthquake size distribution: a historical review and some new approaches. Pure Appl. Geophys. 155 : 2 509– 35 [Google Scholar]
- Utsu T . 1999b . Seismicity Studies: A Conprehensive Review Tokyo: Univ. Tokyo Press [Google Scholar]
- Utsu T . 2002 . Relationships between magnitude scales. International Handbook of Earthquake and Engineering Seismology 81 Part A, ed. WHK Lee, H Kanamori, PC Jennings, C Kisslinger 733– 46 London/San Diego: Academic/Elsevier [Google Scholar]
- Utsu T , Ogata Y , Matsu'ura RS . 1995 . The centenary of the Omori formula for a decay law of aftershock activity. J. Phys. Earth 43 : 1– 33 [Google Scholar]
- Varotsos P , Alexopoulos K , Nomicos K , Lazaridou M . 1986 . Earthquake predictions and electric signals. Nature 322 : 120 [Google Scholar]
- Vere-Jones D . 1970 . Stochastic models for earthquake occurrence. J. R. Stat. Soc. B 32 : 1– 62 [Google Scholar]
- Vere-Jones D . 1978 . Earthquake prediction—a statistician's view. J. Phys. Earth 26 : 129– 46 [Google Scholar]
- Vere-Jones D . 1999 . Probabilities and in formation gain for earthquake forecasting. Comput. Seismol. 30 : 248– 63 [Google Scholar]
- Vere-Jones D , Davies RB . 1966 . A statistical survey of earthquakes in the main seismic region of New Zealand: Part 2—Time series analyses. N. Zealand J. Geol. Geophys. 9 : 251– 84 [Google Scholar]
- von Seggern D , Alexander SS , Baag CE . 1981 . Seismicity parameters preceding moderate to major earthquakes. J. Geophys. Res. 86 : B10 9325– 51 [Google Scholar]
- Wang T , Zhuang J , Kato T , Bebbington M . 2013 . Assessing the potential improvement in short-term earthquake forecasts from incorporation of GPS data. Geophys. Res. Lett. 40 : 2631– 35 [Google Scholar]
- Whittle P . 1962 . Gaussian estimation in stationary time series. Bull. Int. Stat. Inst. 39 : 105– 29 [Google Scholar]
- Wiemer S , Wyss M . 1997 . Mapping the frequency-magnitude distribution in asperities: an improved technique to calculate recurrence times?. J. Geophys. Res. 102 : B7 15115– 28 [Google Scholar]
- Woessner J , Hainzl S , Marzocchi W , Werner MJ , Lombardi AM . et al. 2011 . A retrospective comparative forecast test on the 1992 Landers sequence. J. Geophys. Res. 116 : B05305 [Google Scholar]
- Wong KC , Wyss M . 1985 . Clustering of foreshocks and preshocks in the Circum-Aegean region. Earthq. Predict. Res. 1 : 121– 40 [Google Scholar]
- Wyss M , Shimazaki K , Wiemer S . 1997 . Mapping active magma chambers by b values beneath the off-Ito volcano, Japan. J. Geophys. Res. 102 : B9 20413– 22 [Google Scholar]
- Yamashina K . 1981a . A method of probability prediction for earthquakes in Japan. J. Phys. Earth 29 : 9– 22 [Google Scholar]
- Yamashina K . 1981b . Some empirical rules on foreshocks and earthquake prediction. Earthquake Prediction: An International Review DW Simpson, PG Richards 517– 26 Washington, DC: Am. Geophys. Union [Google Scholar]
- Zhuang J , Jiang C . 2012 . Scoring annual earthquake predictions in China. Tectonophysics 524 : 155– 64 [Google Scholar]
- Zhuang J , Ogata Y , Vere-Jones D . 2002 . Stochastic declustering of space-time earthquake occurrences. J. Am. Stat. Assoc. 97 : 369– 80 [Google Scholar]
- Zhuang J , Ogata Y , Vere-Jones D , Ma L , Guan H . 2014 . Statistical modeling of earthquake occurrences based on external geophysical observations: with an illustrative application to the ultra-low frequency ground electric signals observed in the Beijing region. Seismic Imaging, Fault Rock Damage and Healing Y Li 351– 76 Berlin/Boston: de Gruyter/High. Educ. Press [Google Scholar]
Data & Media loading...
Supplementary Data
Download Supplemental Materials (PDF).
- Article Type: Review Article
Most Read This Month
Most cited most cited rss feed, geologic evolution of the himalayan-tibetan orogen, chemical geodynamics, tectonic implications of the composition of volcanic arc magmas, global glacial isostasy and the surface of the ice-age earth: the ice-5g (vm2) model and grace, oxygen and hydrogen isotopes in the hydrologic cycle, laboratory-derived friction laws and their application to seismic faulting, arc assembly and continental collision in the neoproterozoic east african orogen: implications for the consolidation of gondwanaland, united plates of america, the birth of a craton: early proterozoic assembly and growth of laurentia, biogenic manganese oxides: properties and mechanisms of formation, a tale of amalgamation of three permo-triassic collage systems in central asia: oroclines, sutures, and terminal accretion.
- History & Society
- Science & Tech
- Biographies
- Animals & Nature
- Geography & Travel
- Arts & Culture
- Games & Quizzes
- On This Day
- One Good Fact
- New Articles
- Lifestyles & Social Issues
- Philosophy & Religion
- Politics, Law & Government
- World History
- Health & Medicine
- Browse Biographies
- Birds, Reptiles & Other Vertebrates
- Bugs, Mollusks & Other Invertebrates
- Environment
- Fossils & Geologic Time
- Entertainment & Pop Culture
- Sports & Recreation
- Visual Arts
- Demystified
- Image Galleries
- Infographics
- Top Questions
- Britannica Kids
- Saving Earth
- Space Next 50
- Student Center
- Introduction & Top Questions
- Reservoir induction
- Seismology and nuclear explosions
- Surface phenomena
- Intensity scales
- Earthquake magnitude
- Earthquake energy
- Tectonic associations
- Shallow, intermediate, and deep foci
- Aftershocks, foreshocks, and swarms
- Principal types of seismic waves
- Properties of seismic waves
- Seismographs and accelerometers
- Ocean-bottom measurements
- Measuring microseisms
- Earthquake observatories
- Locating earthquake epicentres
- Observation and interpretation of precursory phenomena
- Interpreting recorded ground motions
- Constructing seismic hazard maps
- Developing resistant structures
- Seismological tomography
- Structure of the Earth’s interior
- Long-period oscillations of the globe
- Extraterrestrial seismic phenomena
- Major historical earthquakes
Why is an earthquake dangerous?
What are earthquake waves, how is earthquake magnitude measured, where do earthquakes occur.
Our editors will review what you’ve submitted and determine whether to revise the article.
- Biology LibreTexts - Earthquake
- Michigan Tech - UPSeis - Where Do Earthquakes Happen?
- USGS - The Science of Earthquakes
- University of Saskatchewan - Physical Geology - What is an Earthquake?
- earthquake - Children's Encyclopedia (Ages 8-11)
- earthquake - Student Encyclopedia (Ages 11 and up)
- Table Of Contents
Over the centuries, earthquakes have been responsible for millions of deaths and an incalculable amount of damage to property. Depending on their intensity, earthquakes (specifically, the degree to which they cause the ground’s surface to shake) can topple buildings and bridges , rupture gas pipelines and other infrastructure, and trigger landslides , tsunamis , and volcanoes . These phenomena are primarily responsible for deaths and injuries. Very great earthquakes occur on average about once per year.
Earthquake waves, more commonly known as seismic waves , are vibrations generated by an earthquake and propagated within Earth or along its surface. There are four principal types of elastic waves: two, primary and secondary waves, travel within Earth, whereas the other two, Rayleigh and Love waves, called surface waves, travel along its surface. In addition, seismic waves can be produced artificially by explosions.
Magnitude is a measure of the amplitude (height) of the seismic waves an earthquake’s source produces as recorded by seismographs . Seismologist Charles F. Richter created an earthquake magnitude scale using the logarithm of the largest seismic wave’s amplitude to base 10. Richter’s scale was originally for measuring the magnitude of earthquakes from magnitudes 3 to 7, limiting its usefulness. Today the moment magnitude scale, a closer measure of an earthquake’s total energy release, is preferred.
Earthquakes can occur anywhere, but they occur mainly along fault lines (planar or curved fractures in the rocks of Earth’s crust ), where compressional or tensional forces move rocks on opposite sides of a fracture. Faults extend from a few centimetres to many hundreds of kilometres. In addition, most of the world’s earthquakes occur within the Ring of Fire , a long horseshoe-shaped belt of earthquake epicentres , volcanoes , and tectonic plate boundaries fringing the Pacific basin .
Recent News
earthquake , any sudden shaking of the ground caused by the passage of seismic waves through Earth ’s rocks. Seismic waves are produced when some form of energy stored in Earth’s crust is suddenly released, usually when masses of rock straining against one another suddenly fracture and “slip.” Earthquakes occur most often along geologic faults , narrow zones where rock masses move in relation to one another. The major fault lines of the world are located at the fringes of the huge tectonic plates that make up Earth’s crust. ( See the table of major earthquakes.)
Little was understood about earthquakes until the emergence of seismology at the beginning of the 20th century. Seismology , which involves the scientific study of all aspects of earthquakes, has yielded answers to such long-standing questions as why and how earthquakes occur.
About 50,000 earthquakes large enough to be noticed without the aid of instruments occur annually over the entire Earth. Of these, approximately 100 are of sufficient size to produce substantial damage if their centres are near areas of habitation. Very great earthquakes occur on average about once per year. Over the centuries they have been responsible for millions of deaths and an incalculable amount of damage to property.
The nature of earthquakes
Causes of earthquakes.
Earth’s major earthquakes occur mainly in belts coinciding with the margins of tectonic plates. This has long been apparent from early catalogs of felt earthquakes and is even more readily discernible in modern seismicity maps, which show instrumentally determined epicentres. The most important earthquake belt is the Circum-Pacific Belt , which affects many populated coastal regions around the Pacific Ocean —for example, those of New Zealand , New Guinea , Japan , the Aleutian Islands , Alaska , and the western coasts of North and South America . It is estimated that 80 percent of the energy presently released in earthquakes comes from those whose epicentres are in this belt. The seismic activity is by no means uniform throughout the belt, and there are a number of branches at various points. Because at many places the Circum-Pacific Belt is associated with volcanic activity , it has been popularly dubbed the “Pacific Ring of Fire .”
A second belt, known as the Alpide Belt , passes through the Mediterranean region eastward through Asia and joins the Circum-Pacific Belt in the East Indies . The energy released in earthquakes from this belt is about 15 percent of the world total. There also are striking connected belts of seismic activity, mainly along oceanic ridges —including those in the Arctic Ocean , the Atlantic Ocean , and the western Indian Ocean —and along the rift valleys of East Africa . This global seismicity distribution is best understood in terms of its plate tectonic setting .
Natural forces
Earthquakes are caused by the sudden release of energy within some limited region of the rocks of the Earth . The energy can be released by elastic strain , gravity, chemical reactions, or even the motion of massive bodies. Of all these the release of elastic strain is the most important cause, because this form of energy is the only kind that can be stored in sufficient quantity in the Earth to produce major disturbances. Earthquakes associated with this type of energy release are called tectonic earthquakes.
An official website of the United States government
Here's how you know
Official websites use .gov A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
- Digg
Latest Earthquakes | Chat Share Social Media
What is an earthquake and what causes them to happen?
An earthquake is caused by a sudden slip on a fault. The tectonic plates are always slowly moving, but they get stuck at their edges due to friction. When the stress on the edge overcomes the friction, there is an earthquake that releases energy in waves that travel through the earth's crust and cause the shaking that we feel.
In California there are two plates - the Pacific Plate and the North American Plate. The Pacific Plate consists of most of the Pacific Ocean floor and the California Coast line. The North American Plate comprises most the North American Continent and parts of the Atlantic Ocean floor. The primary boundary between these two plates is the San Andreas Fault. The San Andreas Fault is more than 650 miles long and extends to depths of at least 10 miles. Many other smaller faults like the Hayward (Northern California) and the San Jacinto (Southern California) branch from and join the San Andreas Fault Zone.
The Pacific Plate grinds northwestward past the North American Plate at a rate of about two inches per year. Parts of the San Andreas Fault system adapt to this movement by constant "creep" resulting in many tiny shocks and a few moderate earth tremors. In other areas where creep is NOT constant, strain can build up for hundreds of years, producing great earthquakes when it finally releases.
Learn More: Glossary of earthquake terms
Related Content
- Publications
Foreshocks, aftershocks - what's the difference?
"Foreshock" and "aftershock" are relative terms. Foreshocks are earthquakes that precede larger earthquakes in the same location. An earthquake cannot be identified as a foreshock until after a larger earthquake in the same area occurs. Aftershocks are smaller earthquakes that occur in the same general area during the days to years following a larger event or "mainshock." They occur within 1-2...
Can the position of the moon or the planets affect seismicity? Are there more earthquakes in the morning/in the evening/at a certain time of the month?
Earthquakes are equally as likely to occur in the morning or the evening. Many studies in the past have shown no significant correlations between the rate of earthquake occurrence and the semi-diurnal tides when using large earthquake catalogs. Several recent studies, however, have found a correlation between earth tides (caused by the position of the moon relative to the earth) and some types of...
What is surface faulting or surface rupture in an earthquake?
Surface rupture occurs when movement on a fault deep within the earth breaks through to the surface. NOT ALL earthquakes result in surface rupture.
At what depth do earthquakes occur? What is the significance of the depth?
Earthquakes occur in the crust or upper mantle, which ranges from the earth's surface to about 800 kilometers deep (about 500 miles). The strength of shaking from an earthquake diminishes with increasing distance from the earthquake's source, so the strength of shaking at the surface from an earthquake that occurs at 500 km deep is considerably less than if the same earthquake had occurred at 20...
Why are there so many earthquakes in the Geysers area in Northern California?
The Geysers Geothermal Field is located in a tectonically active region of Northern California. The major seismic hazards in the region are from large earthquakes occurring along regional faults that are located miles away from the geothermal field, such as the San Andreas and Healdsburg-Rodgers Creek faults. However, activities associated with the withdrawal of steam for producing electric power...
Do earthquakes occur in Antarctica?
Earthquakes do occur in Antarctica, but not very often. There have been some big earthquakes--including one magnitude 8.1 --in the Balleny Islands (between Antarctica and New Zealand). The boundary between the Scotia Plate and the Antarctic Plate just grazes the north tip of the Antarctic Peninsula (look "northwest" from the Pole toward South America). There is also a hint of a line of seismicity...
Where can I find earthquake educational materials?
Start with our Earthquake Hazards Education site. That includes: Earthquakes for Kids Cool Earthquake Facts Earthquake Science for Everyone Other good starting points include: State Geological Surveys for states in earthquake-prone regions The Great ShakeOut Earthquake Drills website SAGE (Seismological Facility for the Advancement of Geoscience), which is a non-profit consortium
Can we cause earthquakes? Is there any way to prevent earthquakes?
Earthquakes induced by human activity have been documented at many locations in the United States and in many other countries around the world. Earthquakes can be induced by a wide range of causes including impoundment of reservoirs, surface and underground mining, withdrawal of fluids and gas from the subsurface, and injection of fluids into underground formations. While most induced earthquakes...
What is the difference between aftershocks and swarms?
Aftershocks are a sequence of earthquakes that happen after a larger mainshock on a fault. Aftershocks occur near the fault zone where the mainshock rupture occurred and are part of the "readjustment process” after the main slip on the fault. Aftershocks become less frequent with time, although they can continue for days, weeks, months, or even years for a very large mainshock. A swarm, on the...
Earthquake-damaged building
Building damanged by the February 2011 earthquake in Christchurch, New Zealand.
What was Pangea?
Listen to hear the answer.
What is the "Ring of Fire"?
Natural hazards science at the u.s. geological survey, earthquake hazards: a national threat, quaternary fault and fold database for the nation, the severity of an earthquake, this dynamic earth: the story of plate tectonics, the interior of the earth, our changing continent, "living in earthquake country: los angeles and the big one" - dr. lucy jones presentation at la natural history museum.
An official website of the United States government
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
- Publications
- Account settings
- My Bibliography
- Collections
- Citation manager
Save citation to file
Email citation, add to collections.
- Create a new collection
- Add to an existing collection
Add to My Bibliography
Your saved search, create a file for external citation management software, your rss feed.
- Search in PubMed
- Search in NLM Catalog
- Add to Search
Hypothesis testing and earthquake prediction
Affiliation.
- 1 Southern California Earthquake Center, University of California, Los Angeles, CA 90095-1567, USA.
- PMID: 11607663
- PMCID: PMC39435
- DOI: 10.1073/pnas.93.9.3772
Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.
PubMed Disclaimer
Similar articles
- Earthquake Shaking and Damage to Buildings: Recent evidence for severe ground shaking raises questions about the earthquake resistance of structures. Page RA, Joyner WB, Blume JA. Page RA, et al. Science. 1975 Aug 22;189(4203):601-8. doi: 10.1126/science.189.4203.601. Science. 1975. PMID: 17838741
- A probabilistic neural network for earthquake magnitude prediction. Adeli H, Panakkat A. Adeli H, et al. Neural Netw. 2009 Sep;22(7):1018-24. doi: 10.1016/j.neunet.2009.05.003. Epub 2009 May 21. Neural Netw. 2009. PMID: 19502005
- Real-time forecasts of tomorrow's earthquakes in California. Gerstenberger MC, Wiemer S, Jones LM, Reasenberg PA. Gerstenberger MC, et al. Nature. 2005 May 19;435(7040):328-31. doi: 10.1038/nature03622. Nature. 2005. PMID: 15902254
- Geological and historical evidence of irregular recurrent earthquakes in Japan. Satake K. Satake K. Philos Trans A Math Phys Eng Sci. 2015 Oct 28;373(2053):20140375. doi: 10.1098/rsta.2014.0375. Philos Trans A Math Phys Eng Sci. 2015. PMID: 26392616 Review.
- An alternative foundation for the planning and evaluation of linkage analysis. II. Implications for multiple test adjustments. Strug LJ, Hodge SE. Strug LJ, et al. Hum Hered. 2006;61(4):200-9. doi: 10.1159/000094775. Epub 2006 Jul 27. Hum Hered. 2006. PMID: 16877867 Review.
- Science. 1985 Aug 16;229(4714):619-24 - PubMed
- Science. 1987 Jun 19;236(4808):1563-7 - PubMed
LinkOut - more resources
Full text sources.
- Europe PubMed Central
- PubMed Central
- Citation Manager
NCBI Literature Resources
MeSH PMC Bookshelf Disclaimer
The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.
COMMENTS
The hypothesis is that these ULVZs scatter seismic waves, which allowed scattered waves to arrive before the primary wave. ... For example, PcP waves begin as a primary wave (P-wave) and deflect ...
The hypothesis test involves calculating the distributions of R for the test hypothesis (that is, assuming that c j is a sample of the process with probabilities p j), and for the null hypothesis (assuming the c j correspond to p j). The log-likelihood ratio will be normally distributed if the log-likelihood functions for both the test and null ...
Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the ...
Earthquake predictions have been based on a variety of seismic and non-seismic phenomena, including animal behavior [6, 16, 23, 24]; water level, temperature 1. ... For example, the null hypothesis might use a poor model for seismicity. Or the null hypothesis might not account for how the
Prediction of time and location of an earthquake of a certain magnitude can be classified into the temporal and spatial accuracy categories listed in Table 1.Note that there is a wider variety of possible combinations in the table than just the usually considered temporal "short-term" and spatial "exact" accuracy. In view of (1) the complexities of the Earth's lithosphere; (2) its ...
Project Ideas. Sources/Usage: Public Domain. View Media Details. An example of a seismic wave. (Public domain.) Earthquake prediction Investigate and find out if anyone has a consistent successful way to predict earthquakes. If not, why were they not successful. Come up with your own idea for predicting earthquakes, and test it.
But this is the crucial factor in earthquake safety. Let's compare Salt Lake City, UT and Port-au-Prince, Haiti. When Salt Lake City has its expected 7.0 magnitude earthquake, worst case scenario is that 3,000-4,000 will die. But on January 12, 2010, Haiti experienced a 7.0 magnitude earthquake that killed nearly 300,000 people.
Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis.
The null hypothesis in assessing earthquake predictions is often, loosely speaking, that the successful predictions are chance coincidences. To make this more precise requires specifying a chance model for the predictions and/or the seismicity. ... (electrical signals for example) helps to predict earthquakes. References, & . , , , - . . , ...
Testing earthquake predictions Brad Luen and Philip B. Stark1 University of California, Berkeley Abstract: Statisticaltests of earthquake predictions require a null hypothesis to model occasional chance successes. To define and quantify 'chance success' is knotty. Some null hypotheses ascribe chance to the Earth: Seismicity is mod-eled as ...
(The three flow mechanisms—cataclasis, gliding, and recrystallization—cannot always be distinguished either.) Examples of experimental boudins are presented to illustrate these categories of deformation. The field evidence is that earthquakes are accompanied by shearing displacements and are therefore due to faulting in the general sense.
These approaches are still often adopted in earthquake forecasting even if many studies, for example, (Kagan & Jackson, 1991; Rong et al., 2003), have shown that the seismic cycle model can be rejected with a high confidence level and, as stated by Mulargia et al. , "no recent work makes a strong data-based case in support of these ...
Earthquake prediction is a branch of the science of seismology concerned with the specification of the time, location, and magnitude of future earthquakes within stated limits, [1] [a] and particularly "the determination of parameters for the next strong earthquake to occur in a region". [2] Earthquake prediction is sometimes distinguished from earthquake forecasting, which can be defined as ...
The seismic gap hypothesis was celebrated when Nishenko 1989 published a list of 13 earthquakes that fit, at least approximately, ... Proponents of the seismic gap theory cite many examples in which identified gaps have been filled by earthquakes. The positive examples are appealing but insufficient for two reasons. First, the definitions of ...
We present evidence that earthquakes are non-linear, chaotic, scale-invariant phenomena. The most probable consequence of earthquake self-similarity is a lack of earthquake predictability as popularly defined, that is a forecast of a specific individual earthquake. Many small earthquakes occur throughout any seismic zone, demonstrating that the ...
For example, several long-term forecasts have been issued on the basis of the 'seismic gap' hypothesis. However, when these forecasts were ... D.D. Hypothesis testing and earthquake prediction. Proc.
Statistical methods and various models in time-space-magnitude parameter space of earthquakes are being developed to analyze seismic activity based on earthquake hypocenter catalogs that are routinely accumulated. Considering complex geophysical environments and uncertainties, we seek proper stochastic modeling that depends on the history of earthquake occurrences and relevant geophysical ...
University of California, Berkeley. Abstract: Statistical tests of earthquake predictions require a null hypothesis to model occasional chance successes. To define and quantify 'chance success' is knotty. Some null hypotheses ascribe chance to the Earth: Seismicity is mod-eled as random.
Earthquakes are caused by the sudden release of energy within some limited region of the rocks of the Earth. The energy can be released by elastic strain, gravity, chemical reactions, or even the motion of massive bodies. Of all these the release of elastic strain is the most important cause, because this form of energy is the only kind that ...
1. Introduction. Even though earthquakes cannot be precisely predicted, their potential consequences must not come as a surprise. In the last decades, seismic hazard and risk assessments have considerably improved [1] allowing to estimate the impact of earthquakes either in advance in terms of stochastic scenarios or shortly after the shaking in the form of rapid impact assessment.
example, but large main-shocks also cluster3. The seismic gap theory asserts The seismic gap theory asserts that large, quasi-periodic 'characteristic earthquakes' deplete stress energy,
An earthquake is caused by a sudden slip on a fault. The tectonic plates are always slowly moving, but they get stuck at their edges due to friction. When the stress on the edge overcomes the friction, there is an earthquake that releases energy in waves that travel through the earth's crust and cause the shaking that we feel. In California there are two plates - the Pacific Plate and the ...
An example of a well-defined earthquake prediction is: I expect at least one earthquake between 1 March 2011 and 11 March 2011 with epicenter(s) in the latitude-longitude range 30N to 34N, 118W to 114W; qualifying earthquakes will have moment magnitude between 5.5 and 6.2 as reported in the Advanced National Seismic System . earthquake catalog
Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the ...