Earthquake magnitudes depend on seismic history, as revealed by a neural network analysis

TL;DR

This paper introduces MAGNET, a neural network model that leverages seismic history to predict earthquake magnitudes more accurately than traditional models, revealing that magnitudes are not independent of seismic history.

Contribution

The study demonstrates that earthquake magnitudes can be predicted from seismic history using a neural network, challenging the assumption of magnitude independence in forecasting models.

Findings

MAGNET achieves an average information gain of 0.07 bits per earthquake.

The model outperforms Gutenberg-Richter-based models in multiple catalogs.

Seismic history contains extractable information about future earthquake magnitudes.

Abstract

Earthquake occurrence is notoriously difficult to predict. While some aspects of their spatiotemporal statistics can be relatively well captured by point-process models, very little is known regarding the magnitude of future events, and it is deeply debated whether it is possible to predict the magnitude of an earthquake before it starts. Most operational forecasting models assume that earthquake magnitudes follow a time-independent Gutenberg-Richter (GR) distribution, effectively treating magnitudes as independent of seismic history. We address this fundamental question by demonstrating that standard hypocenter catalogs carry information about future earthquake magnitudes, making them more predictable than previously considered. We present MAGNET (MAGnitude Neural EsTimation model), which uses a multi-encoder neural network architecture with LSTM units to process spatiotemporal patterns in seismic history. By analyzing hypocenter locations, occurrence times, and magnitudes of past events, MAGNET generates probabilistic magnitude forecasts that demonstrate information gains in predicting magnitudes of future events over GR-based models, after controlling for detection artifacts. Our model achieves an information gain of approximately 0.07 bit per earthquake on average over the GR benchmark in Southern California, Japan, and New Zealand catalogs, with this advantage persisting. These results demonstrate that hypocentral earthquake catalogs contain extractable information about future magnitudes, challenging the conventional separability assumption in earthquake forecasting and offering new approaches for seismic hazard assessment.