Deep Learning for Higher-Order Aftershock Forecasting in Near-Real-Time

Abstract

The use of machine learning (ML) methods for earthquake forecasting has recently emerged as a promising avenue, with several recent publications exploring the application of neural point processes. Such models, in contrast to those currently applied in practice, offer the flexibility to incorporate additional datasets alongside earthquake catalogs, indicating potential for enhanced earthquake forecasting capabilities in the future. However, with a forecasting performance that currently remains similar to that of the agreed-upon benchmark, the Epidemic-Type Aftershock Sequence (ETAS) model, the black-box nature of ML models poses a challenge in communicating forecasts to lay audiences. The ETAS model has stood the test of time and is relatively simple and comprehensively understood, with few empirically derived laws describing aftershock triggering behavior. A main drawback of ETAS is its reliance on large numbers of simulations of possible evolutions of ongoing earthquake sequences, which is typically associated with long computation times or resources required for parallelization. In this study, we propose a deep learning approach to emulate the output of the well-established ETAS model, bridging the gap between traditional methodologies and the potential advantages offered by machine learning. By focusing on modeling the temporal behavior of higher-order aftershocks, our approach aims to combine the interpretability of the ETAS model with the computational efficiency intrinsic to deep learning. Evaluated using commonly applied metrics of both the ML and earthquake forecasting communities, our approach and the traditional, simulation-based approach are shown to perform very similarly in describing synthetic datasets generated with the simulation-based approach. Our method has two major benefits over the traditional approach. It is faster by several orders of magnitude, and it is not susceptible to being influenced by the presence (or absence) of individual 'extreme' realizations of the process, and thus enables accurate earthquake forecasting in near-real-time.