Integrating artifical intelligence and geophysical insights for earthquake forecasting: A cross-disciplinary review

Earthquake forecasting remains one of the most formidable scientific challenges, as current methods still fall short of delivering the reliability needed to yield clear and actionable societal benefits. Traditional models, primarily based on past seismicity and geomechanical data, struggle to capture the complexity of seismic patterns and often overlook valuable non-seismic precursors such as geophysical, geochemical, and atmospheric anomalies. The integration of such diverse data sources into forecasting models, combined with advancements in AI technologies, offers a promising path forward. AI methods, particularly deep learning, excel at processing complex, large-scale datasets, identifying subtle patterns, and handling multidimensional relationships, making them well-suited for overcoming the limitations of conventional approaches.

This review highlights the importance of combining AI with geophysical knowledge to create robust, physics-informed forecasting models. It explores current AI methods, input data types, loss functions, and practical considerations for model development, offering guidance to both geophysicists and AI researchers. Many AI-based studies on earthquake prediction tend to oversimplify the problem, often overlooking crucial aspects like data imbalance and spatio-temporal clustering. However, integrating specialized geophysical knowledge into AI models offers a promising path to overcome these limitations.

We emphasize the importance of interdisciplinary collaboration, urging geophysicists to experiment with AI architectures thoughtfully and encouraging AI experts to deepen their understanding of seismology. By bridging these disciplines, we can develop more accurate, reliable, and societally impactful earthquake forecasting tools.