Seismic travel-time tomography based on Ensemble Kalman Inversion

Yunduo Li; Yijie Zhang; Xueyu Zhu; Jinghuai Gao

doi:10.1093/gji/ggae329

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Seismic travel-time tomography based on Ensemble Kalman Inversion

Journal article

Open access

Peer reviewed

Seismic travel-time tomography based on Ensemble Kalman Inversion

Yunduo Li, Yijie Zhang, Xueyu Zhu and Jinghuai Gao

Geophysical journal international, Vol.240(1), pp.290-302

11/08/2024

DOI: 10.1093/gji/ggae329

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Abstract

Abstract In this paper, we present a new seismic travel-time 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 neural network parameterization, 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).

Neural networks, fuzzy logic

Probability distributions

Statistical methods

Eikonal equation

Seismic tomography

Details

Title: Subtitle: Seismic travel-time tomography based on Ensemble Kalman Inversion
Creators: Yunduo Li - Xi'an Jiaotong University
Yijie Zhang - Xi'an Jiaotong University
Xueyu Zhu - University of Iowa
Jinghuai Gao - Xi'an Jiaotong University
Resource Type: Journal article
Publication Details: Geophysical journal international, Vol.240(1), pp.290-302
Publisher: OXFORD UNIV PRESS
DOI: 10.1093/gji/ggae329
ISSN: 0956-540X
eISSN: 1365-246X
Grant note: National Natural Science Foundation of China: 42174137 National Natural Science Foundation of China: 504054 Simons Foundation: 2020YFA0713400 National Key R&D Program of China
YL and YZ would like to thank National Natural Science Foundation of China (42174137). XZ was supported by the Simons Foundation (504054). The work of JG is supported by National Key R&D Program of China (2020YFA0713400).
Language: English
Electronic publication date: 09/10/2024
Date published: 11/08/2024
Academic Unit: Mathematics
Record Identifier: 9984702950002771

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