AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE025010144
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Thin-layer weak signal enhancement based on cyclic spectrum enhancement technology

Hua Zhang1 Yu Song1 Wei Chen2*
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1 The National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, Nanchang, Jiangxi, China
2 Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University (Ministry of Education & Hubei Province), Wuhan, Hubei, China
Received: 31 December 2025 | Revised: 17 February 2026 | Accepted: 12 March 2026 | Published online: 8 May 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/ )
Abstract

With the continuous growth of global energy demand, subtle reservoirs such as thin beds have become an important target for exploration and development. However, the identification accuracy of thin-layer seismic weak signals is limited by the physical resolution limit of traditional seismic exploration methods due to its small thickness, strong heterogeneity and significant interlayer interference effect. In order to solve the problem of weak signal recognition in thin layer, a method of weak signal recognition in thin layer based on cyclic spectrum enhancement technology is proposed in this paper, improving the sensitivity and accuracy of weak signal detection. The method preprocesses the original seismic data by Gauss filtering to suppress random noise while accurately preserving the main features of the signal, and then introduces even-order derivative operation, the high-frequency details of the weak signal in the thin layer are enhanced, and the reflection difference between the layers is highlighted. To mitigate the high-frequency artifacts inherently generated by high-order derivatives, a Butterworth low-pass filter is employed for directional spectral conditioning, facilitating the high-fidelity separation of the effective signal from noise. Finally, the optimal derivative order is dynamically determined through an adaptive termination criterion to prevent over-enhancement or under-enhancement. Numerical simulation and field seismic data test showed that the proposed method significantly improves the resolution of thin-layer horizons and enhances the detectability of subtle seismic signals.

Keywords
Differential operator
Thin layer
Weak signal
Forward simulation
Oil and gas exploration
Funding
This work was supported in part by the Foundation National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing under Grant 2024QZ-TD-13, in part by the National Natural Science Foundation of China under Grant 42564006, and in part by the Natural Science Foundation of Jiangxi Province under Grant 20242BAB26051.
Conflict of interest
All authors declare no conflicts of interest.
References
  1. Luo HM, Song WQ, Xing YR, Wang CJ, Mu X. Seismic weak signal enhancement processing method based on improved empirical mode decomposition. Prog Geophys. 2019;34(1):167-173. [In Chinese]. doi: 10.6038/pg2019BB0402

 

  1. Gong D, Feng L, Xiao-Ting L, et al. The Application of S-transform Spectrum Decomposition Technique in Extraction of Weak Seismic Signals. Chin J Geophys. 2016;59(1):43-53. doi: 10.1002/cjg2.20212

 

  1. Fan M, Wu S, Hu G, Qu J, Zhang J. A new method to enhance the characterisation of seismically thin beds based on the generalised S transform maximum modulus. Explor Geophys. 2018;49(4):559-571. doi: 10.1071/EG16123

 

  1. Paksima S, Radad M, Roshandel Kahoo A, Soleimani Monfared MS. Identification of thin gas reservoir in reflection seismic data by synchrosqueezing S-transform in time-frequency representation. Arab J Geosci. 2023;16(6):376. doi: 10.1007/s12517-023-11464-4

 

  1. Zhao Y, Cao H, Yang Z, Xu H, Nie R, Wang Z, et al. A seismic thin-layer detection factor calculated by integrated S transform with non-negative matrix factorization. Geophys Prospect. 2024;72(6):2274-2281. doi: 10.1111/1365-2478.13517

 

  1. Wang SY, Chen H, Hu Y, Chen XP. Multichannel seismic resolution enhancement via spectral fitting for thin reservoir characterization. Pet Sci. 2025;22(7):2818-2827. doi: 10.1016/j.petsci.2025.04.008

 

  1. Countiss ML. Frequency-enhanced imaging of stratigraphically complex, thin-bed reservoirs: A case study from South Marsh Island Block 128 Field. Leading Edge. 2002;21(9):826-836. doi: 10.1190/1.1508943

 

  1. Chen S, Liu P, Tang D, Tao S, Zhang T. Identification of thin-layer coal texture using geophysical logging data: Investigation by Wavelet Transform and Linear Discrimination Analysis. Int J Coal Geol. 2021;239:103727. doi: 10.1016/j.coal.2021.103727

 

  1. Yang L, Chen W, Wang H, Chen Y. Deep Learning Seismic Random Noise Attenuation via Improved Residual Convolutional Neural Network. IEEE Trans Geosci Remote Sens. 2021;59(9):7968-7981. doi: 10.1109/TGRS.2021.3053399

 

  1. Song H, Gao Y, Chen W, Xue Y, Zhang H, Zhang X. Seismic random noise suppression using deep convolutional autoencoder neural network. J Appl Geophys. 2020;178:104071. doi: 10.1016/j.jappgeo.2020.104071

 

  1. Cheng Z, Yuan Y, Liu S, Fan C, Li Z, Li Y. Application of wideband wavelet deconvolution for tight oil exploration. Geophys Prospect Pet. 2023;62(1):119-129. [In Chinese]. doi: 10.3969/j.issn.1000-1441.2023.01.010

 

  1. Meng D, Wang D, Feng F, Huang F, Zhu H. Sparse deconvolution based on the Curvelet transform. Acta Pet Sin. 2013;34(1):107-114. [In Chinese]. doi: 10.7623/syxb201301012

 

  1. Zhou X, Li Y, Song X, Jin L, Wang X. Thin Reservoir Identification Based on Logging Interpretation by Using the Support Vector Machine Method. Energies. 2023;16(4):1638. doi: 10.3390/en16041638

 

  1. Chopra S, Castagna J, Portniaguine O. Thin-bed reflectivity inversion. In: SEG Technical Program Expanded Abstracts 2006. 76th SEG Annual International Meeting; October 1-6, 2006; New Orleans, LA. Society of Exploration Geophysicists; 2006:2057-2061. doi: 10.1190/1.2369941

 

  1. Sui Y, Ma J. Blind sparse-spike deconvolution with thin layers and structure. Geophysics. 2020;85(6):V481-V496. doi: 10.1190/geo2019-0423.1

 

  1. Xu Y, Yuan S, Zeng H, et al. Frequency-dependent multiscale network for seismic high-resolution processing. Geophysics. 2025;90(4):V297-V312. doi: 10.1190/geo2023-0682.1

 

  1. Yuan S, Xu Y, Xie R, Chen S, Yuan J. Multi-scale intelligent fusion and dynamic validation for high-resolution seismic data processing in drilling. Pet Explor Dev. 2025;52(3):680- 691. doi: 10.1016/S1876-3804(25)60596-9

 

  1. Li M, Cao H, Yang Z, Yu Y, Ge Q, Yuan S. Intelligent prestack multitrace seismic inversion constrained by probabilistic geologic information. Geophysics. 2025;90(2):IM15-IM34. doi: 10.1190/geo2023-0638.1

 

  1. Ge Q, Cao H, Yang Z, et al. High-resolution seismic impedance inversion integrating the closed-loop convolutional neural network and geostatistics: An application to the thin interbedded reservoir. J Geophys Eng. 2022;19(3):550-561. doi: 10.1093/jge/gxac035

 

  1. Zhang R, Castagna J. Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. Geophysics. 2011;76(6):R147-R158. doi: 10.1190/geo2011-0103.1

 

  1. Song H, Chen W. A method to predict reservoir parameters based on convolutional neural network-gated recurrent unit (CNN-GRU). Pet Geol Recovery Effic. 2019;26(5):73-78. [In Chinese]. doi: 10.13673/j.cnki.cn37-1359/te.2019.05.009

 

  1. Yang L, Chen W, Zha B. Prediction and application of reservoir porosity by convolutional neural network. Prog Geophys. 2019;34(4):1548-1555. [In Chinese]. doi: 10.6038/pg2019CC0528

 

  1. Liu J, Cao J, Zhao L, You J, Li H. Super-resolution reconstruction of seismic images based on deep residual channel attention mechanism. IEEE Access. 2024;12:149032- 149044. doi: 10.1109/ACCESS.2024.3477984

 

  1. Ni W, Liu S, Wang L, Han B, Sheng S. Wavelet shaping deconvolution based on deep learning. Oil Geophys Prospect. 2023;58(6):1313-1321. [In Chinese]. doi: 10.13810/j.cnki.issn.1000-7210.2023.06.002

 

  1. Manzoor U, Ehsan M, Hussain M, Bashir Y. Improved reservoir characterization of thin beds by advanced deep learning approach. Appl Comput Geosci. 2024;23:100188. doi: 10.1016/j.acags.2024.100188

 

  1. Chen W, Zhang D, Chen Y. Random noise reduction using a hybrid method based on ensemble empirical mode decomposition. J Seism Explor. 2017;26(3):227-249.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing