AI 中文总结
通过范围综述，梳理了物理信息机器学习在地震波传播建模中的应用，涵盖正演与反演问题，并评估了其计算效率与精度，指出了当前方法的局限性。
AI 中文摘要
背景：标准数值方法能准确模拟地震波，但计算成本高，尤其对于反问题。机器学习方法被提出作为替代方案，可在保持可接受的物理精度的同时降低计算成本。目标：描绘基于偏微分方程的物理信息机器学习方法如何应用于地震波传播建模。方法：使用OpenAlex和Scopus数据库进行范围综述。所选研究按问题类型（正演或反演）和机器学习策略分类，以识别研究趋势、方法模式和文献空白。结果：物理信息机器学习已应用于地震学中的正演建模和反演，通常以较低的计算成本达到与标准数值方法相当的精度。识别了三种融入物理知识的机制：观测偏差、归纳偏差和学习偏差。为评估代表性方法的方法可重复性，在PyTorch中复现了原始PINN框架，获得的结果与原始报告一致，且在大多数情况下更准确。从综述文献来看，在基准测试一致性、训练成本以及扩展到三维和实验验证问题方面仍存在局限性。结论：标准数值方法仍是地震学工作流程的基础，而物理信息机器学习提供了互补的方法，适用于反问题和替代建模。未来工作应聚焦于一致的基准测试、混合公式以及在真实地球物理条件下的验证。
英文摘要
Standard numerical methods accurately simulate seismic waves but are computationally expensive, particularly for inverse problems. Some machine-learning-based alternatives have emerged, promising to reduce cost while preserving physical accuracy. This scoping review, drawing on OpenAlex and Scopus, maps physics-informed machine learning applications to seismic wave propagation based on partial differential equations, classifying selected studies by problem type (forward or inverse) and learning strategy to uncover trends, patterns, and gaps. Results show that these methods have been applied to both forward modeling and inversion, often matching standard numerical accuracy at lower cost. We identified three mechanisms for incorporating physical knowledge into models: observational, inductive, and learning bias. We implemented a PyTorch replication of the original PINN framework to test methodological reproducibility of a representative method, obtaining results consistent with and, in most cases, more accurate than those originally reported. Based on the reviewed literature, we identified limitations in benchmarking consistency, training cost, and scalability to three-dimensional and experimentally validated problems. We conclude that while standard numerical methods remain foundational, physics-informed machine learning serves as a complementary tool valuable for inverse problems and surrogate modeling, with future work needing consistent benchmarking, hybrid formulations, and validation under realistic geophysical conditions. The continued development of these methods, particularly through hybrid formulations and reproducible benchmarking, may broaden their role in seismological workflows, although realizing this potential will need addressing current limitations in scalability.
