Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California

Ebel, John E.; Chambers, Daniel W.; Kafka, Alan L.; Baglivo, Jenny A.

doi:10.1785/gssrl.78.1.57

Cited by 71 publications

(52 citation statements)

References 16 publications

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“…Two additional 5-year model classes were introduced to account for corrected versions of the models by EBEL et al (2007). In their initial submission, the forecasts were erroneous at some locations; they were replaced by a corrected version on 12 November 2006.…”

Section: Corrected Forecast Groupsmentioning

confidence: 99%

“…Because of the logic of truly prospective testing, the mainshock class and the mainshock?aftershock class were expanded into two groups each. The first group includes all initial RELM submissions and compares them to observations from 1 January 2006 forward, while the second group (denoted by a ''corrected'' suffix) covers all initial submissions and the corrected version of the model by EBEL et al (2007). Because the corrected versions were submitted later, testing for this group started at the submission date of the corrected versions.…”

Section: Corrected Forecast Groupsmentioning

confidence: 99%

“…Seismic hazard analysis requires two fundamental components: an earthquake forecast that describes the probabilities of earthquake occurrence in a spatiotemporal volume; and a ground-motion model that transforms each forecasted event into a site-specific estimate of ground-shaking. RELM participants focused on the former component and developed several earthquake forecast models (BIRD and LIU, 2007;CONSOLE et al, 2007;EBEL et al, 2007;GERSTENBERGER et al, 2007;HELMSTETTER et al, 2007;HOLLIDAY et al, 2007;KAGAN et al, 2007;PETERSEN et al, 2007;RHOADES, 2007;SHEN et al, 2007;WARD, 2007;. These models span a broad range of input data and methods: most are based on past seismicity, however some incorporate geodetic data and/or geological insights.…”

Section: Introductionmentioning

confidence: 99%

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First Results of the Regional Earthquake Likelihood Models Experiment

Schorlemmer

Zechar

Werner

et al. 2010

Pure Appl. Geophys.

111

101

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Abstract-The ability to successfully predict the future behavior of a system is a strong indication that the system is well understood. Certainly many details of the earthquake system remain obscure, but several hypotheses related to earthquake occurrence and seismic hazard have been proffered, and predicting earthquake behavior is a worthy goal and demanded by society. Along these lines, one of the primary objectives of the Regional Earthquake Likelihood Models (RELM) working group was to formalize earthquake occurrence hypotheses in the form of prospective earthquake rate forecasts in California. RELM members, working in small research groups, developed more than a dozen 5-year forecasts; they also outlined a performance evaluation method and provided a conceptual description of a Testing Center in which to perform predictability experiments. Subsequently, researchers working within the Collaboratory for the Study of Earthquake Predictability (CSEP) have begun implementing Testing Centers in different locations worldwide, and the RELM predictability experiment-a truly prospective earthquake prediction effort-is underway within the U.S. branch of CSEP. The experiment, designed to compare time-invariant 5-year earthquake rate forecasts, is now approximately halfway to its completion. In this paper, we describe the models under evaluation and present, for the first time, preliminary results of this unique experiment. While these results are preliminary-the forecasts were meant for an application of 5 years-we find interesting results: most of the models are consistent with the observation and one model forecasts the distribution of earthquakes best. We discuss the observed sample of target earthquakes in the context of historical seismicity within the testing region, highlight potential pitfalls of the current tests, and suggest plans for future revisions to experiments such as this one.

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Section: Corrected Forecast Groupsmentioning

confidence: 99%

Section: Corrected Forecast Groupsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

First Results of the Regional Earthquake Likelihood Models Experiment

Schorlemmer

Zechar

Werner

et al. 2010

Pure Appl. Geophys.

111

101

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“…The Bird and Liu forecast (12) was based on a kinematic model of neotectonics. The Ebel et al forecast (13) was based on the average rate of M ≥ 5 earthquakes in 3°× 3°cells for the period 1932 to 2004. The Helmstetter et al forecast (14) was based on the extrapolation of past seismicity.…”

Section: Resultsmentioning

confidence: 99%

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California

Lee

Turcotte

Holliday

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ≥ 4.95 earthquakes in 0.1°× 0.1°cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ≥ 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M ¼ 7.2, April 4, 2010, El MayorCucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.earthquake forecasting | forecast verification | earthquake clustering R eliable short-term earthquake prediction does not appear to be possible at this time. This was confirmed by the failure to observe any precursory phenomena prior to the 2004 Parkfield earthquake (1). However, earthquakes do not occur randomly in space and time. Large earthquakes occur preferentially in regions where small earthquakes occur. Earthquakes on active faults occur quasiperiodically in time.Earthquakes obey several scaling laws. One example is Gutenberg-Richter frequency-magnitude scaling (2). The cumulative number of earthquakes, N c , with magnitudes greater than M in a region over a specified period of time is well approximated by the relationwhere b is a near universal constant in the range 0.8 < b < 1.1 and a is a measure of the level of seismicity. Small earthquakes can be used to determine a, and Eq. 1 can be used to forecast the probability of occurrence of larger earthquakes. An alternative approach to quantifying earthquake hazard is to specify the recurrence statistics of earthquakes on mapped faults. Geodetic observations can be used to determine rates of strain accumulation, and paleoseismic studies can be used to determine the occurrence of past earthquakes. A problem with this approach is that many damaging earthquakes do not occur on mapped faults.A pattern informatics (PI) approach to earthquake forecasting has been proposed (3-5). In forecasting M ≥ 5 earthquakes, a region is divided into a grid of 0.1°× 0.1°subregions. The rates of seismicity in the subregions are studied to quantify anomalous behavior. Precursory changes that include either increases or decreases in seismicity are identified during a prescribed time interval. If changes exceed a prescribed threshold, hot spots are defined. The forecast is that future M ≥ 5 earthquakes will occur in the hot-spot regions in a ...

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“…The scientific bases of the RELM forecasts have been widely discussed elsewhere and are not the emphasis of this paper, so we refer you to the RELM special issue (Bird & Liu 2007;Ebel et al 2007;Helmstetter et al 2007;Holliday et al 2007;Kagan et al 2007;Shen et al 2007;Ward 2007;) and the summary articles by Schorlemmer et al (2010b) and Zechar et al (2013). For map-view representations of each forecast and the testing region, see Zechar et al's (2013) figs 1 and 2.…”

Section: Datamentioning

confidence: 99%

A parimutuel gambling perspective to compare probabilistic seismicity forecasts

Zechar

Zhuang

2014

Geophysical Journal International

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S U M M A R YUsing analogies to gaming, we consider the problem of comparing multiple probabilistic seismicity forecasts. To measure relative model performance, we suggest a parimutuel gambling perspective which addresses shortcomings of other methods such as likelihood ratio, information gain and Molchan diagrams. We describe two variants of the parimutuel approach for a set of forecasts: head-to-head, in which forecasts are compared in pairs, and round table, in which all forecasts are compared simultaneously. For illustration, we compare the 5-yr forecasts of the Regional Earthquake Likelihood Models experiment for M4.95+ seismicity in California.

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Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California

Cited by 71 publications

References 16 publications

First Results of the Regional Earthquake Likelihood Models Experiment

First Results of the Regional Earthquake Likelihood Models Experiment

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California

A parimutuel gambling perspective to compare probabilistic seismicity forecasts

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