Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation
It addresses the need for robust and interpretable AI in operational seismic and volcanic monitoring, but is incremental as it organizes and highlights existing methods rather than introducing new ones.
This paper surveys machine learning approaches for seismic and volcanic signal analysis, focusing on improving reliability under domain shifts, providing uncertainty estimates, and connecting outputs to physical constraints.
Modern seismic and volcanic monitoring is increasingly shaped by continuous, multi-sensor observations and by the need to extract actionable information from nonstationary, noisy wavefields. In this context, machine learning has moved from a research curiosity to a practical ingredient of processing chains for detection, phase picking, classification, denoising, and anomaly tracking. However, improved accuracy on a fixed dataset is not sufficient for operational use. Models must remain reliable under domain shift (new stations, changing noise, evolving volcanic activity), provide uncertainty that supports decision-making, and connect their outputs to physically meaningful constraints. This paper surveys and organizes recent ML approaches for seismic and volcanic signal analysis, highlighting where classical signal processing provides indispensable inductive bias, how self-supervision and generative modeling can reduce dependence on labels, and which evaluation protocols best reflect transfer across regions. We conclude with open challenges for robust, interpretable, and maintainable AI-assisted monitoring.