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Transposed arrangement strategy-based deep learning for seismic data crossline interpolation

Jiyun Yu1† ,  Yonghwan Joo2† ,  Daeung Yoon1*
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1 Department of Energy and Resources Engineering, Chonnam National University, Gwangju, Republic of Korea
2 Resources Exploration and Development Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Republic of Korea
JSE, 025330056 https://doi.org/10.36922/JSE025330056
Submitted: 13 August 2025 | Revised: 24 October 2025 | Accepted: 3 November 2025 | Published: 25 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

Towed-streamer marine seismic acquisition systems generally have a dense receiver spacing in the inline receiver direction within common-shot gathers (along-streamer), while the streamer spacing is relatively sparse in the crossline receiver direction within common-shot gathers (cross-streamer). This disparity can lead to spatial aliasing issues in the crossline receiver direction within common-shot gathers and result in resolution degradation during the processing of 3D seismic data. To address this issue and enhance resolution, data interpolation in the crossline receiver direction within common-shot gathers is essential. Various supervised learning-based interpolation methods have been developed to this end. However, the absence of true data in the crossline receiver direction within common-shot gathers poses challenges for training supervised learning models with actual field data. To overcome this, we have developed a novel approach called the “transposed arrangement strategy” for a deep learning-based reconstruction model for crossline interpolation. This method involves training the model with 3D input and labels patched from existing field data, and then applying the trained model with transposed 3D input to reconstruct data in the crossline receiver direction within common-shot gathers. During this process, the 3D U-Net and U-Net+ models were utilized, demonstrating their superiority through comparisons with traditional interpolation methods.

Keywords
Data processing
Signal processing
Deep learning
Crossline interpolation
Transposed arrangement strategy
3D U-Net+
Funding
This work was supported by the Korea Institute of Marine Science and Technology Promotion grant funded by the Ministry of Oceans and Fisheries (20220254, Development of technology for seabed classification based on machine learning).
Conflict of interest
The authors declare 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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