Machine Learning for Earthquake Prediction: Techniques for Seismic Forecasting and Disaster Preparedness

Abstract

Earthquakes remain among the most difficult natural hazards to forecast, and decades of research have shown that deterministic prediction of the precise time, location, and magnitude of an individual earthquake is not currently achievable with any known method. Machine learning has nonetheless become an increasingly important tool across the broader task of seismic forecasting, supporting probabilistic hazard estimation, rapid earthquake early warning, aftershock sequence forecasting, and automated detection of subtle seismic signals that traditional methods struggle to identify. This paper surveys the application of machine learning to earthquake-related forecasting and disaster preparedness, distinguishing carefully between deterministic prediction, which remains largely unattainable, and probabilistic forecasting and rapid detection tasks, where machine learning has demonstrated measurable practical value. We review deep learning methods for seismic waveform analysis and phase picking, statistical and learned approaches to aftershock forecasting, and applications to earthquake early warning, induced seismicity monitoring, and infrastructure risk assessment. We present a case study illustrating a machine-learning-based earthquake early warning pipeline, discuss benchmarks used to evaluate seismic machine learning systems, and examine the persistent challenges of data imbalance, generalization across tectonic regions, and the risk of conflating improved detection capability with genuine predictive capability. We conclude with future directions connecting seismic machine learning with physics-informed modeling and multi-hazard disaster response systems.