AccScience Publishing / JSE / Volume 35 / Issue 3 / DOI: 10.36922/JSE026120048
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CT-UNet: Curvelet transform-based component-wise deep learning framework for seismic trace interpolation

Jiyun Yu1 ,  Young Kim2 ,  Daeung Yoon1*
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1 Department of Energy and Resources Engineering, Chonnam National University, Gwangju, Republic of Korea
2 YK Geophysics, Seoul, Republic of Korea
JSE 2026, 35(3), 026120048 https://doi.org/10.36922/JSE026120048
Received: 16 March 2026 | Revised: 27 May 2026 | Accepted: 27 May 2026 | Published online: 15 June 2026
© 2026 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

Seismic data may suffer from missing traces due to economic and environmental constraints as well as equipment malfunctions, where regular gaps caused by receiver or streamer spacing and irregular gaps resulting from topographic limitations occur simultaneously. Such missing data degrade the reliability of subsequent processing and interpretation, making accurate interpolation essential. High-resolution reconstruction requires balanced recovery of broadband components spanning low to high frequencies; however, existing deep learning-based interpolation methods exhibit degraded performance in certain spectral regions. In this study, we propose the curvelet transform (CT)-UNet, a deep learning interpolation method that leverages CT-based frequency- and directional-component separation to improve reconstruction balance across frequency bands, particularly under high-rate, regular undersampling conditions. The proposed method applies the uniform discrete CT (UDCT) to decompose input data into low-frequency and high-frequency components with multi-resolution and directional characteristics, trains specialized U-Net models for each component, and reconstructs the final result through inverse transformation. Performance was evaluated on synthetic data (Society of Exploration Geophysicists/European Association of Geoscientists and Engineers Salt Model) and field data (Mobil Amplitude Versus Offset Viking Graben Line 12) by comparing to f–x interpolation, Constrained Diffusion-Driven Deep Image Prior, a standard U-Net, a parameter-matched U-Net-Wide, and the wavelet-based wavelet transform-UNet. An exploratory experiment on irregular missing and a structural compatibility analysis of the UDCT level configuration were also conducted. Results show that CT-UNet improves reconstruction quality across the tested missing conditions, with the clearest gains observed under high-rate regular undersampling and the 70% irregular missing scenario. Shot-level analysis further supports its advantage in challenging cases, particularly for synthetic 80%, field 75%, field 80%, and 70% irregular missing conditions. The advantage is especially pronounced in recovering curvature-varying, conflicting-dip events, while structural similarity is generally maintained at a level comparable to or higher than that of the baseline methods.

Keywords
Seismic data interpolation
Curvelet transform
Deep learning
U-Net
Uniform discrete curvelet transform
Missing trace reconstruction
Funding
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. RS-2025-25435564), and by the Korea Institute of Marine Science and Technology Promotion grant funded by the Ministry of Oceans and Fisheries (No. 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, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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