AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE025350063
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GMLAN: Grouped-residual and multi-scale large-kernel attention network for seismic image super-resolution

Anxin Zhang1† ,  Zhenbo Guo2†* ,  Shiqi Dong1† ,  Zhiqi Wei2
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1 Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology (Ministry of Education), School of Electrical Engineering, Northeast Electric Power University, Jilin, China
2 Bureau of Geophysical Prospecting National Petroleum Corporation, Zhuozhou, Hebei, China
JSE, 025350063 https://doi.org/10.36922/JSE025350063
Submitted: 25 August 2025 | Revised: 17 October 2025 | Accepted: 23 October 2025 | Published: 17 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The resolution of seismic images significantly impacts the accuracy of subsequent seismic interpretation and reservoir location. However, the resolution of seismic images often degrades due to the influence of multiple factors, making super-resolution of seismic images essential and critical. We propose a grouped-residual and multi-scale large-kernel attention network (GMLAN) framework, trained on synthetic seismic images to achieve excellent seismic image super-resolution on field seismic data. GMLAN is primarily composed of two modules: The feature extraction module (FEM) and the image reconstruction module (IRM). The FEM consists of two components: Shallow feature extraction (SFE) and deep feature extraction (DFE). The SFE component is designed to capture the basic information of seismic images, such as large-scale structures and morphological features of the strata. The DFE component serves as the cornerstone of the feature extraction process, leveraging residual groups and multi-scale large-kernel attention to distill detailed features from seismic images, such as stratigraphic interfaces, dip angles, and relative amplitudes. Finally, the IRM utilizes sub-pixel convolution, a learnable upsampling technique, to reconstruct super-resolution seismic images while preserving the continuity of seismic features. The framework demonstrates satisfactory performance on both synthetic and field data.

Keywords
Seismic images
Super-resolution
Deep learning
Grouped-residual structures
Malti-scale large-kernel self-attention
Funding
This work was supported by BGP’s Science and Technology project (01-04-02-2024).
Conflict of interest
The authors declare that they have no competing interests.
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Journal of Seismic Exploration, Print ISSN: 0963-0651, Published by AccScience Publishing
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