🤖 AI Summary
This study addresses the challenges of weak model generalization, lack of physical consistency, and insufficient uncertainty quantification in detecting seismic and volcanic signals under non-stationary, high-noise conditions. To overcome these limitations, the work integrates physical priors with machine learning by embedding classical signal processing as an inductive bias, leveraging self-supervised and generative modeling to reduce reliance on labeled data, and introducing a more realistic cross-regional transfer evaluation protocol. The proposed approach significantly enhances model generalization, interpretability, and decision reliability in phase picking and anomaly detection tasks—particularly in unseen stations, time-varying noise environments, and evolving volcanic activity—thereby advancing the practical deployment of AI in geophysical monitoring.
📝 Abstract
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.