106 GSA Geophysics and Geodynamics Division 50th Anniversary Special Session and George Woollard Lecture II

Monday, 10 October 2022
1:30 PM - 5:30 PM
205

WHEN’S THE NEXT EARTHQUAKE? A MORE REALISTIC MODEL OF EARTHQUAKE RECURRENCE AND PROBABILITY

Paper number: 106-3
Program: Special Sessions
Time: 2:20 PM – 2:45 PM

Authors

Abstract

Current models of earthquake recurrence have two major limitations. First, they predict that the probability of a large earthquake stays constant or even decreases after it is “overdue” (past the expected average recurrence interval), so the additional accumulated strain does not make an earthquake more likely. Second, the models assume that large earthquakes release all accumulated strain, despite evidence of partial strain release in earthquake histories showing temporal clusters and gaps. These limitations arise because the models are purely statistical, assuming that future earthquakes will satisfy a probability distribution that describes the times between past large earthquakes. Thus they describe average behavior well, but not deviations from it, because they do not incorporate fundamental aspects of the strain accumulation and release processes that cause earthquakes. Here we calculate earthquake probabilities using the Long-Term Fault Memory (LTFM) model, which better reflects the strain accumulation and release processes. Using the southern San Andreas fault as an example, we show that LTFM yields a more realistic earthquake forecast. Whereas current models estimate the earthquake probability will be essentially unchanged in the next 80 years, LTFM predicts that the probability will continue to grow, resulting in a 30-year earthquake probability that is 38% higher than the other models. By allowing partial strain release, LTFM incorporates the specific timing of past earthquakes, which commonly used probability models cannot do. Thus LTFM better forecasts the exceptionally short inter-event time before the 1857 Fort Tejon earthquake. Although LTFM is more complex than existing models, it is also more powerful.

Geological Society of America Abstracts with Programs. Vol. 54, No. 6

doi: 10.1130/abs/2022AM-377871

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