The Seismic Wavefield Common Task Framework
Alexey Yermakov, Yue Zhao, Marine Denolle, Yiyu Ni, Philippe M. Wyder, Judah Goldfeder, Stefano Riva, Jan Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles Cranmer, J. Nathan Kutz

TL;DR
This paper introduces a standardized framework for evaluating machine learning methods on seismic wavefield data, addressing challenges in earthquake forecasting and ground motion prediction.
Contribution
It presents a Common Task Framework with curated datasets and metrics for fair, rigorous comparison of ML algorithms in seismology.
Findings
Demonstrated the framework on three seismic datasets.
Evaluated various methods for wavefield reconstruction from sparse data.
Showcased the framework's ability to reveal strengths and limitations of algorithms.
Abstract
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…
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