ARTIFICIAL INTELLIGENCE MODELS FOR EARTHQUAKE PREDICTION AND SEISMIC HAZARD ASSESSMENT

Abstract

Earthquake prediction and seismic hazard assessment remain among the most challenging problems in geophysics due to the complex, non-linear, and multi-scale nature of lithospheric dynamics. Traditional statistical models (Gutenberg-Richter law, ETAS) provide valuable long-term probabilistic insights but struggle with short-term forecasting and subtle precursory signals hidden in massive, noisy datasets. Artificial intelligence encompassing supervised learning (SVM, Random Forest, XGBoost), deep learning (LSTM, CNN, TCN), graph neural networks (GNNs), and physics-informed neural networks (PINNs) has emerged as a transformative paradigm, enabling automated catalog development (increasing detected events by up to 10×), high-resolution spatiotemporal modeling, and improved ground-motion prediction. Hybrid frameworks like SeismoQuakeGNN integrate graph-based spatial dependencies with Transformer attention for long-range temporal patterns, while PINNs embed physical laws (wave equation, Eikonal) directly into the loss function for mesh-free simulations and physics-consistent predictions. Multimodal foundation models (SeisModal) fuse waveforms, catalogs, GNSS, ionospheric TEC, geochemical (radon, methane), and satellite data for comprehensive event understanding. Performance benchmarks show LSTM and hybrid models achieving superior RMSE/MAE in magnitude prediction, with SHAP-based explainable AI enhancing interpretability and trust. Regional applications (Western Turkey, Yunnan-Sichuan, Pakistan-Hindukush) demonstrate strong regional generalization when properly tuned. Persistent challenges include spectral bias in PINNs, long training times limiting real-time early warning, dataset quality, modifiable areal unit problem (MAUP), and the need for robust validation against physical baselines. Future directions emphasize multimodal foundation models, integration with IoT/drones/satellites, federated learning, and hybrid physics-AI systems to bridge the gap between data-driven pattern recognition and mechanistic understanding paving the way for more reliable probabilistic hazard assessment and actionable short-term forecasting to enhance societal resilience.