scholarly journals Asperity-based earthquake likelihood models for Italy

2010 ◽  
Vol 53 (3) ◽  
Author(s):  
Laura Gulia ◽  
Stefan Wiemer ◽  
Danijel Schorlemmer
Keyword(s):  
Earthquake Likelihood Models

scholarly journals Implications of the Regional Earthquake Likelihood Models test of earthquake forecasts in California

2012 ◽  
Vol 2 (2) ◽  
pp. 10 ◽  
Author(s):  
Michael Karl Sachs ◽  
Ya-Ting Lee ◽  
Donald Turcotte ◽  
James R. Holliday ◽  
John B. Rundle
Keyword(s):  
Conditional Probability ◽  
Mean Value ◽  
Test Area ◽  
Test Period ◽  
Future Earthquake ◽  
The Mean ◽  
The One ◽  
Earthquake Likelihood Models ◽  
Regional Earthquake

The Regional Earthquake Likelihood Models (RELM) test was the first competitive comparison of prospective earthquake forecasts. The test was carried out over 5 years from 1 January 2006 to 31 December 2010 over a region that included all of California. The test area was divided into 7682 0.1°x0.1° spatial cells. Each submitted forecast gave the predicted numbers of earthquakes <em>N<sub>emi</sub></em> larger than <em>M</em>=4.95 in 0.1 magnitude bins for each cell. In this paper we present a method that separates the forecast of the number of test earthquakes from the forecast of their locations. We first obtain the number <em>N<sub>em</sub></em> of forecast earthquakes in magnitude bin <em>m</em>. We then determine the conditional probability <em>λ<sub>emi</sub></em>=<em>N<sub>emi</sub>/</em><em>N<sub>em</sub></em> that an earthquake in magnitude bin <em>m</em> will occur in cell <em>i</em>. The summation of <em>λ<sub>emi</sub></em> over all 7682 cells is unity. A random (no skill) forecast gives equal values of <em>λ<sub>emi</sub></em> for all spatial cells and magnitude bins. The <em>skill</em> of a forecast, in terms of the location of the earthquakes, is measured by the success in assigning large values of <em>λ<sub>emi</sub></em> to the cells in which earthquakes occur and low values of <em>λ<sub>emi</sub></em> to the cells where earthquakes do not occur. Thirty-one test earthquakes occurred in 27 different combinations of spatial cells <em>i</em> and magnitude bins <em>m</em>, we had the highest value of <em>λ<sub>emi</sub></em> for that <em>mi</em> cell. We evaluate the performance of eleven submitted forecasts in two ways. First, we determine the number of <em>mi</em> cells for which the forecast <em>λ<sub>emi</sub></em> was the largest, the best forecast is the one with the highest number. Second, we determine the mean value of <em>λ<sub>emi</sub></em> for the 27 <em>mi</em> cells for each forecast. The best forecast has the highest mean value of <em>λ<sub>emi</sub></em>. The success of a forecast during the test period is dependent on the allocation of the probabilities λemi between the mi cells, since the sum over the mi cells is unity. We illustrate the forecast distributions of <em>λ<sub>emi</sub></em> and discuss their differences. We conclude that the RELM test was successful in illustrating the choices required when a forecast of the location of a future earthquake is made.

scholarly journals First Results of the Regional Earthquake Likelihood Models Experiment

2010 ◽  
pp. 5-22 ◽  
Author(s):  
Danijel Schorlemmer ◽  
◽  
J. Douglas Zechar ◽  
Maximilian J. Werner ◽  
Edward H. Field ◽  
...  
Keyword(s):  
First Results ◽  
Earthquake Likelihood Models ◽  
Regional Earthquake

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

2011 ◽  
Vol 108 (40) ◽  
pp. 16533-16538 ◽  
Author(s):  
Y.-T. Lee ◽  
D. L. Turcotte ◽  
J. R. Holliday ◽  
M. K. Sachs ◽  
J. B. Rundle ◽  
...  
Keyword(s):  
Earthquake Likelihood Models ◽  
Regional Earthquake

scholarly journals Regional Earthquake Likelihood Models II: Information Gains of Multiplicative Hybrids

2014 ◽  
Vol 104 (6) ◽  
pp. 3072-3083 ◽  
Author(s):  
D. A. Rhoades ◽  
M. C. Gerstenberger ◽  
A. Christophersen ◽  
J. D. Zechar ◽  
D. Schorlemmer ◽  
...  
Keyword(s):  
Earthquake Likelihood Models ◽  
Regional Earthquake

Multiplicative earthquake likelihood models incorporating strain rates

2017 ◽  
pp. ggw486 ◽  
Author(s):  
D.A. Rhoades ◽  
A. Christophersen ◽  
M.C. Gerstenberger
Keyword(s):  
Strain Rates ◽  
Earthquake Likelihood Models

Multiplicative Earthquake Likelihood Models Based on Fault and Earthquake Data

2015 ◽  
Vol 105 (6) ◽  
pp. 2955-2968 ◽  
Author(s):  
D. A. Rhoades ◽  
A. Christophersen ◽  
M. C. Gerstenberger
Keyword(s):  
Earthquake Likelihood Models ◽  
Earthquake Data

Regional Earthquake Likelihood Models I: First-Order Results

2013 ◽  
Vol 103 (2A) ◽  
pp. 787-798 ◽  
Author(s):  
J. D. Zechar ◽  
D. Schorlemmer ◽  
M. J. Werner ◽  
M. C. Gerstenberger ◽  
D. A. Rhoades ◽  
...  
Keyword(s):  
First Order ◽  
Earthquake Likelihood Models ◽  
Regional Earthquake

scholarly journals Evaluating the RELM Test Results

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Michael K. Sachs ◽  
Ya-Ting Lee ◽  
Donald L. Turcotte ◽  
James R. Holliday ◽  
John B. Rundle
Keyword(s):  
Skill Score ◽  
Earthquake Forecasting ◽  
Alternative Methods ◽  
Test Results ◽  
Atmospheric Sciences ◽  
Test Region ◽  
Methods Of Evaluation ◽  
Order Of Magnitude ◽  
Earthquake Likelihood Models ◽  
Verification Methodology

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.

scholarly journals First Results of the Regional Earthquake Likelihood Models Experiment

2010 ◽  
Vol 167 (8-9) ◽  
pp. 859-876 ◽  
Author(s):  
Danijel Schorlemmer ◽  
◽  
J. Douglas Zechar ◽  
Maximilian J. Werner ◽  
Edward H. Field ◽  
...  
Keyword(s):  
First Results ◽  
Earthquake Likelihood Models ◽  
Regional Earthquake
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