Earthquake forecasting and its verification

Holliday, J. R.; Nanjo, Kazuyoshi; Tiampo, K. F.; Rundle, John B.; Turcotte, Donald L.

doi:10.5194/npg-12-965-2005

Cited by 139 publications

(151 citation statements)

References 83 publications

(77 reference statements)

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“…The second method is related to the Relative Operating Characteristic (ROC) used in weather prediction e orts (Jolli e and Stephenson, 2003), where the success rate of an event prediction is compared against the false alarm rate (ibid., p. 69; see also Holliday et al, 2005). Since periodic (diurnal, annual) e ects are strong in weather prediction, such a method has broad applications; we can compare the above characteristics of a forecast system for one-day or one-year alarm periods.…”

Section: Introductionmentioning

confidence: 99%

On Earthquake Predictability Measurement: Information Score and Error Diagram

Kagan

2007

Pure appl. geophys.

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We discuss two methods for measuring the e ectiveness of earthquake prediction algorithms: the information score based on the likelihood ratio and error diagrams. For both of these methods, closed form expressions are obtained for the renewal process based on the gamma and lognormal distributions. The error diagram is more informative than the likelihood ratio and uniquely speci es the information score.We derive an expression connecting the information score and error diagrams. We then obtain the estimate of the region bounds in the error diagram for any value of the information score. We discuss how these preliminary results can be extended for more realistic models of earthquake occurrence.

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Section: Introductionmentioning

confidence: 99%

On Earthquake Predictability Measurement: Information Score and Error Diagram

Kagan

2007

Pure appl. geophys.

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“…Basically the research objective is to reduce risk areas analyzing the historical seismicity for anomalous behaviour. The approach is based on a pattern informatics (PI) method which quantifies temporal variations in seismicity and is as follows Holliday et al, (2005) …”

Section: Related Forecasting Methodologies For Earthquakesmentioning

confidence: 99%

Earthquake Prediction: Analogy with Forecasting Models for Cyber Attacks in Internet and Computer Systems

Pontes¹,

Silva²,

Guelfi³

et al. 2012

Earthquake Research and Analysis - Statistical Studies, Observations and Planning

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“…1. Comparisons of these approaches have come to different conclusions regarding their validity (6,7). These comparisons emphasize the difficulties in evaluating the performance of seismicity forecasts.…”

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confidence: 99%

“…Therefore, this is an alarm-based forecast. Utilizing the PI method, a forecast of California hot spots valid for the period 2000-2010 was given (3); 16 of the 18 earthquakes that occurred during the period 2000-2005 occurred in these hot-spot regions (6).…”

mentioning

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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Earthquake forecasting and its verification

Cited by 139 publications

References 83 publications

On Earthquake Predictability Measurement: Information Score and Error Diagram

On Earthquake Predictability Measurement: Information Score and Error Diagram

Earthquake Prediction: Analogy with Forecasting Models for Cyber Attacks in Internet and Computer Systems

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

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