AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE025450102
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Interpretable intelligent gas-bearing reservoir prediction using time–frequency analysis and manifold-regularized semi-supervised GAN

Shuying Ma1 ,  Junxing Cao2,3* ,  Rong Wang1* ,  Xudong Jiang3 ,  Jun Wang2,3 ,  Lingsen Zhao3 ,  Hong Li3 ,  Xin Tang3
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1 School of Network and Communication Engineering, Chengdu Technological University, Chengdu, Sichuan, China
2 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan, China
3 College of Geophysics, Chengdu University of Technology, Chengdu, Sichuan, China
JSE, 025450102 https://doi.org/10.36922/JSE025450102
Received: 5 November 2025 | Revised: 9 January 2026 | Accepted: 10 February 2026 | Published online: 24 April 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

As hydrocarbon exploration advances toward deep and complex reservoirs, the identification accuracy of traditional time–frequency (TF) analysis is constrained by strongly heterogeneous geological conditions. Concurrently, while deep learning has shown great potential, mainstream supervised models commonly face the dual challenges of scarce labeled samples and the lack of interpretability in their black-box decision-making processes. To address these challenges, this study proposes an innovative, intelligent prediction framework integrating high-precision TF analysis, manifold-regularized semi-supervised generative adversarial networks, and SHapley Additive exPlanations (SHAP) for interpretability. First, the Fourier-based synchrosqueezing transform was employed to extract two-dimensional TF features with superior energy concentration, effectively overcoming the resolution limits imposed by the Heisenberg uncertainty principle. Subsequently, the manifold-regularized semi-supervised generative adversarial network was developed. By incorporating manifold regularization constraints, the discriminator captures the intrinsic topological structure of large-scale unlabeled samples, effectively leveraging data geometry to significantly enhance generalization capability under sparse-label conditions. Finally, the SHAP method was utilized to conduct a post hoc interpretation. Experimental results on the Marmousi II model demonstrate a remarkable testing accuracy of 98.4%. In a real-world application to deep marine reservoirs in the Sichuan Basin, the framework achieved an 85.0% testing accuracy using only 5% labeled samples. Compared to baseline models, the semi-supervised strategy and manifold regularization contributed accuracy gains of 18.8% and 5.0%, respectively. SHAP analysis further confirms the model’s adaptive capability to extract geophysical features, enabling it to deconstruct the tuning-effect patterns in synthetic data and the low-frequency enhancement/high-frequency attenuation patterns in real data, respectively. This validation of geophysical consistency provides a theoretical foundation for the application of artificial intelligence in complex hydrocarbon exploration.

Keywords
Gas-bearing prediction
Semi-supervised learning
Synchrosqueezing transform
Generative adversarial network
SHapley Additive exPlanations
Funding
This work was supported by the National Natural Science Foundation of China (Grant 42330813 and Grant 42030812), and the Natural Science Foundation of Sichuan Province (Grant 2026NSFSC1138).
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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