First Results of the Regional Earthquake Likelihood Models Experiment

Schorlemmer, Danijel; Zechar, J. D.; Werner, M. J.; Field, Edward H.; Jackson, David D.; Jordan, Thomas H.

doi:10.1007/978-3-0346-0500-7_2

Cited by 38 publications

(61 citation statements)

References 34 publications

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“…These tests were applied to the raw submitted data. This approach was applied to the first 2.5 years of RELM results by Schorlemmer et al [13]. Zechar et al [21] recognized a problem with the original proposed likelihood tests and proposed a modification.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

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Evaluating the RELM Test Results

Sachs

Lee

Turcotte

et al. 2012

International Journal of Geophysics

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We consider implications of the Regional Earthquake Likelihood Models (RELM) test results with regard to earthquake forecasting. Prospective forecasts were solicited forM≥4.95earthquakes in California during the period 2006–2010. During this period 31 earthquakes occurred in the test region withM≥4.95. We consider five forecasts that were submitted for the test. We compare the forecasts utilizing forecast verification methodology developed in the atmospheric sciences, specifically for tornadoes. We utilize a “skill score” based on the forecast scoresλfiof occurrence of the test earthquakes. A perfect forecast would haveλfi=1, and a random (no skill) forecast would haveλfi=2.86×10-3. The best forecasts (largest value ofλfi) for the 31 earthquakes had values ofλfi=1.24×10-1toλfi=5.49×10-3. The best mean forecast for all earthquakes wasλ̅f=2.84×10-2. The best forecasts are about an order of magnitude better than random forecasts. We discuss the earthquakes, the forecasts, and alternative methods of evaluation of the performance of RELM forecasts. We also discuss the relative merits of alarm-based versus probability-based forecasts.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

“…The submitted forecasts have been discussed in some detail [13]. The nineteen forecasts submitted by eight groups are available on the RELM website (http://relm.cseptesting .org/).…”

Section: Submitted Forecastsmentioning

confidence: 99%

Evaluating the RELM Test Results

Sachs

Lee

Turcotte

et al. 2012

International Journal of Geophysics

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“…A well‐known example of such a statistical model is the Epidemic‐Type Aftershock Sequence (ETAS) model, in which earthquake occurrence is treated as a point process and space, time, and magnitude kernels are typically assumed to be independent [ Ogata , ]. These types of statistical models have had good success in retrospective and prospective forecasting of moderate‐magnitude earthquakes ( M < 5) [ Schorlemmer et al ., ] but have been criticized for ignoring the physical processes of stress transfer and relaxation. Previous attempts to combine ETAS and finite‐fault‐based models have found that some elements of elastic rebound and stress relaxation must be included in order to prevent self‐exciting aftershock sequences from “blowing up” [ Field , , ].…”

Section: Introductionmentioning

confidence: 99%

Larger aftershocks happen farther away: Nonseparability of magnitude and spatial distributions of aftershocks

Elst

Shaw

2015

Geophysical Research Letters

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Aftershocks may be driven by stress concentrations left by the main shock rupture or by elastic stress transfer to adjacent fault sections or strands. Aftershocks that occur within the initial rupture may be limited in size, because the scale of the stress concentrations should be smaller than the primary rupture itself. On the other hand, aftershocks that occur on adjacent fault segments outside the primary rupture may have no such size limitation. Here we use high‐precision double‐difference relocated earthquake catalogs to demonstrate that larger aftershocks occur farther away than smaller aftershocks, when measured from the centroid of early aftershock activity—a proxy for the initial rupture. Aftershocks as large as or larger than the initiating event nucleate almost exclusively in the outer regions of the aftershock zone. This observation is interpreted as a signature of elastic rebound in the earthquake catalog and can be used to improve forecasting of large aftershocks.

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“…The space and time variations of the seismic parameters in a given region are often considered as indicators of changes in the seismicity regime, eventually announcing the occurrence of a large earthquake (e.g. Schorlemmer et al 2010). Among the seismic parameters whose variations are studied, the most popular are certainly the amplitude, M. Bengoubou-Valérius Bureau de Recherche Géologiques et Minières Guadeloupe, Morne Houëlmont, Route de l'Observatoire, 97113 Gourbeyre, France e-mail: m.bengoubou-valerius@brgm.fr D. Gibert (&) Institut de Physique du Globe de Paris (CNRS UMR 7154), Sorbonne Paris Cité, 1 rue Jussieu, 75238 Paris cedex, France e-mail: gibert@ipgp.fr A, and the exponent, b, of the Gutenberg-Richter (GR) power-law distribution of earthquake magnitudes (Gutenberg and Richter 1944;Main 2000),…”

Section: Introductionmentioning

confidence: 99%

Bootstrap determination of the reliability of b-values: an assessment of statistical estimators with synthetic magnitude series

Bengoubou-Valerius

Gibert

2012

Nat Hazards

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International audienceWe consider some practical issues of the determination of the b-value of sequences of magnitudes with the bootstrap method for short series of length L and various quantization levels Dm of the magnitude. Preliminary Monte Carlo tests performed with Dm ¼ 0 demonstrate the superiority of the maximum likelihood estimator bMLE, and the inconsistency of the, yet often used, bLR estimator defined as the least-squares slope of the experimental Gutenberg-Richter curve. The Monte Carlo tests are also applied to an estimator, bKS, which minimizes the Kolmogorov-Smirnov distance between the cumulative distribution of magnitudes and a power-law model. Monte Carlo tests of discrete versions of the bMLE and bKS estimators are done for Dm ¼ f0:1; 0:2; 0:3g and used as reference to evaluate the performance of the bootstrap determination of b. We show that all estimators provide b estimates within 10 % error for L C 100 and if a large number, n = 2 9 105, of bootstrapped sample series is used. A resolution test done with Dm ¼ 0:1 reveals that a clear distinction between b = 0.8, 1.0, and 1.2 is obtained if L= 200

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First Results of the Regional Earthquake Likelihood Models Experiment

Cited by 38 publications

References 34 publications

Evaluating the RELM Test Results

Evaluating the RELM Test Results

Larger aftershocks happen farther away: Nonseparability of magnitude and spatial distributions of aftershocks

Bootstrap determination of the reliability of b-values: an assessment of statistical estimators with synthetic magnitude series

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