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Evaluating the RELM Test Results
Author(s) -
M. K. Sachs,
Ya-Ting Lee,
Donald L. Turcotte,
J. R. Holliday,
John B. Rundle
Publication year - 2012
Publication title -
international journal of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.253
H-Index - 19
eISSN - 1687-8868
pISSN - 1687-885X
DOI - 10.1155/2012/543482
Subject(s) - algorithm , computer science , artificial intelligence , machine learning
We consider implications of the Regional Earthquake Likelihood Models (RELM) test results with regard to earthquake forecasting. Prospective forecasts were solicited for M≥4.95 earthquakes in California during the period 2006–2010. During this period 31 earthquakes occurred in the test region with M≥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 scores λfi of occurrence of the test earthquakes. A perfect forecast would have λfi=1, and a random (no skill) forecast would have λfi=2.86×10-3. The best forecasts (largest value of λfi) for the 31 earthquakes had values of λfi=1.24×10-1 to λfi=5.49×10-3. The best mean forecast for all earthquakes was λ̅f=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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