Least-squares reverse time migration of simultaneous sources with deep-learning-based denoising

Author:

Wu Bo1ORCID,Yao Gang2ORCID,Ma Xiao3ORCID,Chen Hanming3ORCID,Wu Di3ORCID,Cao Jingjie4ORCID

Affiliation:

1. China University of Petroleum (Beijing), National Key Laboratory of Petroleum Resources and Engineering, Beijing, China; China University of Petroleum (Beijing), China Key Lab of Geophysical Exploration of CNPC, Beijing, China and China University of Petroleum (Beijing), Unconventional Petroleum Research Institute, Beijing, China.

2. China University of Petroleum (Beijing), National Key Laboratory of Petroleum Resources and Engineering, Beijing, China; China University of Petroleum (Beijing), China Key Lab of Geophysical Exploration of CNPC, Beijing, China and China University of Petroleum (Beijing), Unconventional Petroleum Research Institute, Beijing, China. (corresponding author)

3. China University of Petroleum (Beijing), National Key Laboratory of Petroleum Resources and Engineering, Beijing, China; China University of Petroleum (Beijing), China Key Lab of Geophysical Exploration of CNPC, Beijing, China and China University of Petroleum (Beijing), College of Geophysics, Beijing, China.

4. Hebei Geo University, Ministry of Natural Resources, Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Hebei, China.

Abstract

Least-squares reverse time migration (LSRTM) is currently one of the most advanced migration imaging techniques in the field of geophysics. It uses least-squares inversion to fit the observed data, resulting in high-resolution imaging results with more accurate amplitudes and better illumination compensation than conventional reverse time migration (RTM). However, noise in the observed data and the Born approximation forward operator can result in high-wavenumber artifacts in the final imaging results. Moreover, iteratively solving LSRTM leads to one or two orders of computational cost higher than conventional RTM, making it challenging to apply extensively in industrial applications. Simultaneous source acquisition technology can reduce the computational cost of LSRTM by reducing the number of wavefield simulations. However, this technique also can cause high-wavenumber crosstalk artifacts in the migration results. To effectively remove the high-wavenumber artifacts caused by these issues, simultaneous source and deep learning are combined to speed up LSRTM as well as suppress high-wavenumber artifacts. A deep residual neural network (DR-Unet) is trained with synthetic samples, which are generated by adding field noise to synthesized noise-free migration images. Then, the trained DR-Unet is applied on the gradient of LSRTM to remove high-wavenumber artifacts in each iteration. Compared to directly applying DR-Unet denoising to LSRTM results, embedding DR-Unet denoising into the inversion process can better preserve weak reflectors and improve denoising effects. Finally, the proposed LSRTM method is tested on two synthetic data sets and a land data set. The tests demonstrate that the proposed method can effectively remove high-wavenumber artifacts, improve imaging results, and accelerate convergence speed.

Funder

RD Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting application

National Natural Science Foundation of China

Publisher

Society of Exploration Geophysicists

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Geophysics Bright Spots;The Leading Edge;2024-09

2. Enhancing GPR Multisource Reverse Time Migration With a Feature Pyramid Attention Network;IEEE Transactions on Geoscience and Remote Sensing;2024

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