A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology

[2607.00178] A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology Skip to main content Search Submit Donate Log in Physics > Computational Physics arXiv:2607.00178 (physics) [Submitted on 30 Jun 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology Authors: Óscar Rincón-Cardeño , Gregorio Pérez-Bernal , Silvana Montoya-Noguera , Nicolás Guarín-Zapata View PDF HTML (experimental) Abstract: 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. Subjects: Computational Physics (physics.comp-ph) MSC classes: 86-08, 65M99 ACM classes: J.2; G.1.8; I.6.0 Cite as: arXiv:2607.00178 [physics.comp-ph] (or arXiv:2607.00178v2 [physics.comp-ph] for this version) https://doi.org/10.48550/arXiv.2607.00178 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nicolas Guarin-Zapata [ view email ] [v1] Tue, 30 Jun 2026 20:51:40 UTC (1,641 KB) [v2] Thu, 27 Aug 2026 19:18:23 UTC (1,681 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.comp-ph < prev | next > new | recent | 2026-07 Change to browse by: physics References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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