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Enhancing seismic resolution based on U-Net deep learning network
Zeyu Li2 ,
Guoquan Wang2 ,
Chenghong Zhu2,3,4 ,
Shuangquan Chen1
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1
College of Geophysics, China University of Petroleum (Beijing), 102249 Beijing, P.R.China.,
2
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, 100083 Beijing, P.R. China.,
3
Sinopec Key Laboratory of Seismic Elastic Wave Technology, 100083 Beijing, P.R.China.,
4
Sinopec Petroleum Exploration and Production Research Institute, 100083 Beijing, P.R. China.,
JSE 2023, 32(4), 315–336;
Submitted: 6 June 2023 | Accepted: 10 July 2023 | Published: 1 August 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
Deep and ultra-deep reservoirs, unconventional hydrocarbons, and other complex -resolution with high description. Therefore, high-resolution seismic data processing is a challenging and important endeavor. The conventional high-resolution processing techniques, such as inverse Q-filtering technique based on the stratum attenuation model and deconvolution methods, are nearly model-dependent. Deep learning method based on image processing has been widely used in seismic data processing and inversion in recent years, which is a data-driven method and has powerful abilities to extract data features. In this paper, we rebuild a deep learning network based on U-net network and proposed a high-resolution seismic data processing method and workflow. In the network, we utilized ResPath structure instead of the straightforward skip connection in the conventional Res-Unet, and employed a combination loss function by using the mean absolute error and multiscale structural similarity. Moreover, by introducing pre-training strategy into the processing workflow, the proposed network can reduce the loss of low- frequency components and perceive low-frequency information. Synthetic and real field seismic data are used to validate the network, the trained model by using pre-training strategy can better maintain the low-frequency components of the original data and recover the high-frequency components, and the signal-to-noise ratio and resolution of seismic data have both been significantly-enhanced.
Keywords
U-net
deep learning
pre-training strategy
seismic resolution
data-driven
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