A MORE REALISTIC FAULTING MODEL IMPROVES EARTHQUAKE FORECASTS

Current models of earthquake recurrence do not incorporate fundamental aspects of the strain accumulation and release processes on faults that cause earthquakes. As a result, they 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 additional accumulated strain does not make an earthquake more likely. Second, they assume that large earthquakes release all accumulated strain, despite evidence of partial strain release shown by earthquake histories showing clusters and gaps. These limitations arise because current 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. Here we calculate earthquake probabilities using the Generalized Long-Term Fault Memory (GLTFM) model, which better reflects the strain accumulation and release processes. GLTFM assumes that earthquake probability always increases with time between earthquakes as strain accumulates and allows earthquakes releasing only part of the strain accumulated on the fault. GLTFM estimates when residual strain is likely present and its impact on the expected timing of the next earthquake, and so can describe clustered earthquake sequences better than commonly used models. Hence GLTFM yields a more realistic earthquake forecast for the southern San Andreas fault. Whereas current models estimate the earthquake probability will be essentially unchanged in the next 80 years, GLTFM predicts that the probability will continue to grow, resulting in a 30-year earthquake probability that is 38% higher than the other models. Moreover, GLTFM better forecasts the exceptionally short inter-event time before the 1857 Fort Tejon earthquake. GLTFM’s simple implementation and versatility should make it a powerful tool in earthquake forecasting.