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Adaptive learned iterative shrinkage-thresholding algorithm combined with a physics-driven self-supervised method for sparse spike deconvolution

Shunhao Hu1,2 ,  Shulin Pan1* ,  Ziyu Qin1,3 ,  Yinghe Wu4* ,  Yaojie Chen2
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1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan, China
2 Reservoir Geophysical Laboratory, School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan, China
3 Department of Software Engineering, School of Computer Engineering, Chengdu Technological University, Chengdu, Sichuan, China
4 State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
JSE 2026, 35(1), 251–268; https://doi.org/10.36922/JSE025460111
Submitted: 16 November 2025 | Revised: 6 January 2026 | Accepted: 20 January 2026 | Published: 23 February 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

The conventional iterative shrinkage-thresholding algorithm (ISTA) faces several limitations, such as dependence on manual parameter tuning and limited ability to recover weak reflections. Hence, this study proposes a sparse spike deconvolution method based on an adaptive learned ISTA (Ada-LISTA) and a self-supervised physics-driven objective function to overcome these challenges. First, using Ada-LISTA as the backbone, the threshold and step size in the iterative process were dynamically adjusted through its adaptive parameter learning, and the seismic wavelet dictionary was used as the model input to enhance the adaptability to different complex geological scenarios. Then, a self-supervised physics-driven objective function was introduced to jointly optimize the residual of seismic records and the sparsity of reflection coefficients, further improving the interpretability of the model. Finally, comparative experiments were carried out using theoretical simulation data and actual seismic data from the Bohai Bay Basin, China, to evaluate the inversion performance of the proposed method. The experimental results indicate that, compared to the traditional ISTA algorithm, the proposed method achieved marked enhancements in reflection coefficient inversion accuracy, seismic resolution, and robustness against noise. Overall, the proposed method offers an efficient and reliable technical solution for high-resolution seismic inversion and effective recovery of weak reflection signals, providing practical support for interpreting complex subsurface geological structures.

Keywords
Sparse spike deconvolution
Seismic resolution
Weak reflection
Adaptive learned iterative shrinkage-thresholding algorithm
Physics-driven self-supervised method
Funding
This work was supported in part by the Deep Earth Probe and Mineral Resources Exploration–National Science and Technology Major Project (2025ZD1004600), in part by the Natural Science Foundation of Sichuan Province (2024NSFSC0808), and in part by the Talent Project of Chengdu Technological University (2024RC028).
Conflict of interest
The authors declare they have no competing interests.
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