The Seismic Wavefield Common Task Framework
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909005967785984 |
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| author | Yermakov, Alexey Zhao, Yue Denolle, Marine Ni, Yiyu Wyder, Philippe M. Goldfeder, Judah Riva, Stefano Williams, Jan Zoro, David Rude, Amy Sara Tomasetto, Matteo Germany, Joe Bakarji, Joseph Maierhofer, Georg Cranmer, Miles Kutz, J. Nathan |
| author_facet | Yermakov, Alexey Zhao, Yue Denolle, Marine Ni, Yiyu Wyder, Philippe M. Goldfeder, Judah Riva, Stefano Williams, Jan Zoro, David Rude, Amy Sara Tomasetto, Matteo Germany, Joe Bakarji, Joseph Maierhofer, Georg Cranmer, Miles Kutz, J. Nathan |
| contents | Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variability of source locations, mechanisms, and Earth models (e.g., subsurface structure and topography effects). Addressing these with simulations is hindered by their massive scale, both in synthetic data volumes and numerical complexity, while real-data efforts are constrained by models that inadequately reflect the Earth's complexity and by sparse sensor measurements from the field. Recent machine learning (ML) efforts offer promise, but progress is obscured by a lack of proper characterization, fair reporting, and rigorous comparisons. To address this, we introduce a Common Task Framework (CTF) for ML for seismic wavefields, demonstrated here on three distinct wavefield datasets. Our CTF features a curated set of datasets at various scales (global, crustal, and local) and task-specific metrics spanning forecasting, reconstruction, and generalization under realistic constraints such as noise and limited data. Inspired by CTFs in fields like natural language processing, this framework provides a structured and rigorous foundation for head-to-head algorithm evaluation. We evaluate various methods for reconstructing seismic wavefields from sparse sensor measurements, with results illustrating the CTF's utility in revealing strengths, limitations, and suitability for specific problem classes. Our vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets, raising the bar for rigor and reproducibility in scientific ML. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Seismic Wavefield Common Task Framework Yermakov, Alexey Zhao, Yue Denolle, Marine Ni, Yiyu Wyder, Philippe M. Goldfeder, Judah Riva, Stefano Williams, Jan Zoro, David Rude, Amy Sara Tomasetto, Matteo Germany, Joe Bakarji, Joseph Maierhofer, Georg Cranmer, Miles Kutz, J. Nathan Machine Learning Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variability of source locations, mechanisms, and Earth models (e.g., subsurface structure and topography effects). Addressing these with simulations is hindered by their massive scale, both in synthetic data volumes and numerical complexity, while real-data efforts are constrained by models that inadequately reflect the Earth's complexity and by sparse sensor measurements from the field. Recent machine learning (ML) efforts offer promise, but progress is obscured by a lack of proper characterization, fair reporting, and rigorous comparisons. To address this, we introduce a Common Task Framework (CTF) for ML for seismic wavefields, demonstrated here on three distinct wavefield datasets. Our CTF features a curated set of datasets at various scales (global, crustal, and local) and task-specific metrics spanning forecasting, reconstruction, and generalization under realistic constraints such as noise and limited data. Inspired by CTFs in fields like natural language processing, this framework provides a structured and rigorous foundation for head-to-head algorithm evaluation. We evaluate various methods for reconstructing seismic wavefields from sparse sensor measurements, with results illustrating the CTF's utility in revealing strengths, limitations, and suitability for specific problem classes. Our vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets, raising the bar for rigor and reproducibility in scientific ML. |
| title | The Seismic Wavefield Common Task Framework |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.19927 |