Joint 2D Inversion Of Amt And Seismic Travel Time Data With Deep Learning Constraint
Abstract
We apply deep learning techniques to assist the joint inversion of audio-magnetotelluric (AMT) and seismic travel time data. Based on the assumption that the resistivity-velocity relationship is known a priori, deep neural networks are used to learn the nonlinear maps between these two parameters. In this manner, the correlation between resistivity and velocity in both structure and value can be implicitly established through the neural network. During the inversion, we alternatively update resistivity and velocity using the Gauss-Newton method. Moreover, both resistivity and velocity are updated based on the reference model mapped from the other parameter by deep residual convolutional neural networks (DRCNNs). Numerical example shows that this joint inversion scheme can achieve more accurate inversion and converges faster than separate inversion.
Recommended Citation
R. Guo et al., "Joint 2D Inversion Of Amt And Seismic Travel Time Data With Deep Learning Constraint," SEG Technical Program Expanded Abstracts, pp. 1695 - 1699, Society of Exploration Geophysicists, Jan 2020.
The definitive version is available at https://doi.org/10.1190/segam2020-3426298.1
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
1949-4645; 1052-3812
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Society of Exploration Geophysicists, All rights reserved.
Publication Date
01 Jan 2020
