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Application of data augmentation based on generative adversarial network in impedance inversion

PENG WANG HUIQUN XU ZHEN PENG ZEFENG WANG MENGQIONG YANG
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School of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, Hubei Province, P.R. China,
JSE 2023, 32(2), 155–168;
Submitted: 21 August 2022 | Accepted: 4 February 2023 | Published: 1 April 2023
© 2023 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Wang, P., Xu, H.Q., Peng, Z., Wang, Z. and Yang, M.Q., 2023. Application of data augmentation based on generative adversarial network in impedance inversion. Journal of Seismic Exploration, 32: 155-168. In recent years, various deep learning techniques have been widely used in the field of geophysics. As far as seismic impedance inversion is concerned, a nonlinear mapping model from seismic data to wave impedance can be established by training the depth inversion network, and then the impedance information can be predicted by the nonlinear model. However, the effectiveness of the current impedance inversion methods based on deep neural networks depends on the number of labels. The generalization ability of the model trained in the state of few labels is poor. Data augmentation can alleviate this situation by using the existing data. Therefore, the author proposes a method based on generative adversarial network (GAN) to augment the labels in the original data set, and uses geophysical forward modeling technology to forward seismic data to achieve the function of data augmentation. Unlike existing GAN, which generates samples directly from noise, the method augments the labeled data from the original dataset. The validity of this method is verified by model data and actual data. The method provides a data augmentation seismic inversion technique based on GAN for impedance inversion.

Keywords
impedance inversion
temporal convolutional network
data augmentation
generative adversarial network
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing