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The Generalized Long-Term Fault Memory Model and Applications to Paleoseismic Records

Description: 

Paleoseismic studies show large variability in earthquake inter-event times along a fault, with short intervals often separated by long quiescent periods. Some paleoseismologists have interpreted this variability as a product of an earthquake’s partial strain release with the next earthquake occurring sooner than expected because of the remaining residual strain. However, commonly used probabilistic large earthquake recurrence models attribute this variability purely to chance, not the state of strain on the fault. Here, we present an alternative probabilistic model, built on the Long-Term Fault Memory model framework that better reflects the strain accumulation and release process. This Generalized Long-Term Fault Memory model (GLTFM) predicts that this inter-event time variability arises from both chance and the state of strain on the fault. Specifically, it estimates when residual strain is likely present and its impact on the timing of the next earthquake in the sequence. Additionally, GLTFM assumes that additional accumulated strain always increases earthquake probability. In contrast, the commonly used lognormal and Brownian Passage Time models predict that the probability of a large earthquake stays constant or even decreases after it is “overdue” (past the observed average recurrence interval) so additional accumulated strain does not make an earthquake more likely. GLTFM’s simple implementation and versatility should make it a powerful tool in earthquake forecasting.

Session: New Insights into the Development, Testing and Communication of Seismicity Forecasts [Poster Session]

Type: Poster

Date: 5/2/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: James

Student Presenter: No

Invited Presentation: 

Authors