Predictability in the ETAS Model of Interacting Triggered Seismicity

TL;DR

This paper develops an analytical method for earthquake forecasting using the ETAS model, emphasizing the importance of accounting for unobserved triggered seismicity to improve prediction accuracy over short time horizons.

Contribution

It introduces an analytical approach to incorporate unobserved triggered seismicity in the ETAS model for better earthquake rate predictions.

Findings

Strong predictability when forecasting a small fraction of large events.

Prediction accuracy decreases as the fraction of targets increases.

Intrinsic stochasticity limits the forecasting skill in triggered seismicity models.

Abstract

As part of an effort to develop a systematic methodology for earthquake forecasting, we use a simple model of seismicity based on interacting events which may trigger a cascade of earthquakes, known as the Epidemic-Type Aftershock Sequence model (ETAS). The ETAS model is constructed on a bare (unrenormalized) Omori law, the Gutenberg-Richter law and the idea that large events trigger more numerous aftershocks. For simplicity, we do not use the information on the spatial location of earthquakes and work only in the time domain. We offer an analytical approach to account for the yet unobserved triggered seismicity adapted to the problem of forecasting future seismic rates at varying horizons from the present. Tests presented on synthetic catalogs validate strongly the importance of taking into account all the cascades of still unobserved triggered events in order to predict correctly the future level of seismicity beyond a few minutes. We find a strong predictability if one accepts to predict only a small fraction of the large-magnitude targets. However, the probability gains degrade fast when one attempts to predict a larger fraction of the targets. This is because a significant fraction of events remain uncorrelated from past seismicity. This delineates the fundamental limits underlying forecasting skills, stemming from an intrinsic stochastic component in these interacting triggered seismicity models.