SCEC Award Number 04134 View PDF
Proposal Category Collaborative Proposal (Integration and Theory)
Proposal Title Earthquake probabilities based on clustering and stress interactions
Investigator(s)
Name Organization
Agnes Helmstetter University of California, Los Angeles Yan Kagan University of California, Los Angeles David D. Jackson University of California, Los Angeles
Other Participants Lucile Jones, USGS
Matt Gerstenberger, post-doc, USGS
Max Werner, graduate student, UCLA
SCEC Priorities SCEC Groups Source Physics, SHRA
Report Due Date N/A Date Report Submitted N/A
Project Abstract
 We will construct a parametric model of earthquake probability as a function of location, magnitude, time and focal mechanisms and use it to make regular short-term forecasts for Southern California. We will post the forecast on the RELM website and test it alongside other short-term forecasts such as the STEP forecast by Gerstenberger and Jones. Out model will combine the best features of the ETAS (Omori clustering) models with those based on static or dynamic stress interaction. We will provide a systematic approach to optimize parameters for better predictive power and develop tests for use with future earthquakes and comparision with other models. Both the model, and the tests, will use all earthquakes above a magnitude threshold rather than a few case studies. Our triggered seismicity model will be part of the RELM effort to develop and evaluate a variety of viable models, and may also be an integral part of the SCEC system-level ERF. In addition, the comparison of different models of earthquake forecasting will allow us to test several hypotheses to understand better the mechanisms of earthquake interaction. In particular, we will test whether spatial variations of Coulomb stress provide more predictive power than a model based solely on proximity to past earthquakes. We will also test whether cumulative Coulomb stress or dynamic stress better explains earthquake rate changes.