| Autor: |
Li, Yunduo, Zhang, Yijie, Zhu, Xueyu, Gao, Jinghuai |
| Předmět: |
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| Zdroj: |
Geophysical Journal International; Jan2025, Vol. 240 Issue 1, p290-302, 13p |
| Abstrakt: |
In this paper, we present a new seismic traveltime tomography approach that combines ensemble Kalman inversion (EKI) with neural networks (NNs) to facilitate the inference of complex underground velocity fields. Our methodology tackles the challenges of high-dimensional velocity models through an efficient NN parametrization, enabling efficient training on coarse grids and accurate output on finer grids. This unique strategy, combined with a reduced-resolution forward solver, significantly enhances computational efficiency. Leveraging the robust capabilities of EKI, our method not only achieves rapid computations but also delivers informative uncertainty quantification for the estimated results. Through extensive numerical experiments, we demonstrate the exceptional accuracy and uncertainty quantification capabilities of our EKI-NNs approach. Even in the face of challenging geological scenarios, our method consistently generates valuable initial models for full wave inversion (FWI). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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