将物理约束融入机器学习，提升地震火山信号分析的可靠性与可解释性。
Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation
- 融合物理规律的机器学习框架，增强模型在新环境下的适应性。
- 自监督与生成建模减少对标注数据依赖，提升泛化能力。
- 适合地震监测、火山预警等需要高可信度决策的场景。
现代地震与火山监测日益依赖持续的多传感器观测，需从非平稳、噪声干扰的波场中提取可操作信息。在此背景下，机器学习已从研究兴趣发展为检测、相位拾取、分类、去噪及异常追踪等处理流程中的实用工具。然而，仅在固定数据集上提升准确率不足以满足业务需求。模型必须在域偏移（如新台站、噪声变化、火山活动演变）下保持可靠性，提供支持决策的不确定性估计，并使其输出符合物理约束。本文综述并组织了近年来地震与火山信号分析的机器学习方法，强调经典信号处理提供的先验知识不可替代，探讨自监督与生成建模如何降低标签依赖，以及何种评估协议最能反映跨区域迁移性能。最后提出构建鲁棒、可解释、可持续维护的AI辅助监测系统所面临的开放挑战。
原文摘要 · Abstract (English)
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.
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