Coal Geology & Exploration
Abstract
Background Small-scale discontinuous geobodies such as faults, collapse columns, and pinch-out points are widely present in underground spaces. They are closely associated with the safe production and development of underground resources such as coals, oil, and gas. As the wave field responses of small-scale discontinuous geobodies, diffracted waves can overcome the shortcomings of traditional reflected wave imaging, being capable of accurately identifying and localizing small-scale geobodies. Objective and Method This study aims to achieve diffracted wave imaging of discontinuous geobodies. Based on the differences in kinematic and dynamic characteristics between reflected and diffracted waves, this study leveraged the precise self-adaptive decomposition ability in the time and frequency domains of the variational mode decomposition (VMD) method, as well as the efficient and stable global optimization ability of the grey wolf optimizer (GWO). The VMD-GWO algorithm effectively avoided empirical errors and local optimum problems while possessing elevated accuracy of diffracted wave separation and high self-adaptability. Results and Conclusions Compared to the whale optimization algorithm (WOA) and ant colony optimization (ACO), the particle swarm optimization (PSO), sparrow search algorithm (SSA), and GWO yielded lower optimal fitness values (3.172), indicating their higher optimization performance. Moreover, compared to PSO and SSA, GWO demonstrated faster convergence, achieving the global optimum through only six iterations. These results highlight the superiority of GWO in both optimization performance and efficiency. The tests of synthetic and actual data verified that the proposed GWO-VMD algorithm is effective in diffracted wave separation and shows strong suppression of reflected waves, thereby enabling the high-resolution imaging of microscale structures.
Keywords
discontinuous geobody, diffracted wave separation, variational mode decomposition (VMD), parameter optimization, grey wolf optimizer (GWO)
DOI
10.12363/issn.1001-1986.25.05.0353
Recommended Citation
LIN Peng, LIU Yulin, PENG Suping,
et al.
(2026)
"A method for diffracted wave separation and imaging based on the GWA-VMD,"
Coal Geology & Exploration: Vol. 54:
Iss.
2, Article 17.
DOI: 10.12363/issn.1001-1986.25.05.0353
Available at:
https://cge.researchcommons.org/journal/vol54/iss2/17
Reference
[1] ZHOU Jiawei,PENG Suping,LIN Peng,et al. Diffraction separation and imaging using ensemble empirical mode decomposition and multichannel singular spectrum analysis[J]. Geophysical Prospecting,2023,71(2):245−262.
[2] 彭苏萍,杜文凤,赵伟,等. 煤田三维地震综合解释技术在复杂地质条件下的应用[J]. 岩石力学与工程学报,2008,27(增刊1):2760−2765.
PENG Suping,DU Wenfeng,ZHAO Wei,et al. 3D coalfield seismic integrated interpretation technique in complex geological condition[J]. Chinese Journal of Rock Mechanics and Engineering,2008,27(Sup.1):2760−2765.
[3] 李连崇,唐春安,左宇军,等. 煤层底板下隐伏陷落柱的滞后突水机理[J]. 煤炭学报,2009,34(9):1212−1216.
LI Lianchong,TANG Chun’an,ZUO Yujun,et al. Mechanism of hysteretic groundwater inrush from coal seam floor with karstic collapse columns[J]. Journal of China Coal Society,2009,34(9):1212−1216.
[4] 李闯建. 地震绕射波分离与成像方法研究[D]. 北京:中国矿业大学(北京),2021.
LI Chuangjian. Study on seismic diffraction separation and imaging method[D]. Beijing:China University of Mining & Technology (Beijing),2021.
[5] 林朋. 地震绕射波分离与高精度成像方法研究[D]. 北京:中国矿业大学(北京),2020.
LIN Peng. Study on seismic diffraction separation and high–precision imaging method[D]. Beijing:China University of Mining & Technology (Beijing),2020.
[6] 栾锡武,杨佳佳. 地震绕射波波场分离与成像方法综述[J]. 石油物探,2022,61(5):761−770.
LUAN Xiwu,YANG Jiajia. A review of seismic diffraction wavefield separation and imaging methods[J]. Geophysical Prospecting for Petroleum,2022,61(5):761−770.
[7] 曹静杰,许昌昊,朱跃飞. 基于层次聚类多道奇异谱分析的地震数据同时重建与去噪方法[J]. 石油地球物理勘探,2023,58(4):818−829.
CAO Jingjie,XU Changhao,ZHU Yuefei. Simultaneous reconstruction and denoising of seismic data using multi–channel singular spectrum analysis based on hierarchical clustering[J]. Oil Geophysical Prospecting,2023,58(4):818−829.
[8] MARKOVIC M,MALEHMIR,R,MALEHMIR,A. Diffraction pattern recognition using deep semantic segmentation[J]. Near Surface Geophysics,2022,20(5):507−518.
[9] ZHAO Jingtao,SUN Xiuli,PENG Suping,et al. Separating prestack diffractions with SVMF in the flattened shot domain[J]. Journal of Geophysics and Engineering,2019,16(2):389−398.
[10] CLAERBOUT J F. Earth soundings analysis:Processing versus inversion[M]. Boston:Blackwell Scientific Publications,1992.
[11] FOMEL S. Applications of plane–wave destruction filters[J]. Geophysics,2002,67(6):1946−1960.
[12] LI Chuangjian,PENG Suping,CUI Xiaoqin,et al. 3–D pre–stack diffraction separation by extending the PWD method with parametrized local slope[J]. Geophysical Journal International,2023,232(2):750−763.
[13] YU Caixia,WANG Yanfei,ZHAO Jingtao. A seismic diffraction extraction method for the study of discontinuous geologies using a regularisation algorithm[J]. Exploration Geophysics,2017,48(1):49−55.
[14] 吴秋莹,胡斌,刘财,等. 基于L1/2正则化的抛物线Radon变换多次波压制方法[J]. 吉林大学学报(地球科学版),2024,54(1):323−336.
WU Qiuying,HU Bin,LIU Cai,et al. Multiple suppression method of parabolic Radon transform based on L1/2 regularization[J]. Journal of Jilin University (Earth Science Edition),2024,54(1):323−336.
[15] KLOKOV A,BAINA R,LANDA E,et al. Diffraction imaging for fracture detection:Synthetic case study[C]//SEG Technical Program Expanded Abstracts 2010. Society of Exploration Geophysicists,2010:3354–3358.
[16] KARIMPOULI S,HASSANI H,MALEHMIR A,et al. Understanding the fracture role on hydrocarbon accumulation and distribution using seismic data:A case study on a carbonate reservoir from Iran[J]. Journal of Applied Geophysics,2013,96:98−106.
[17] LI Chuangjian,PENG Suping,ZHAO Jingtao,et al. Polarity–preserved diffraction extracting method using modified apex–shifted Radon transform and double–branch Radon transform[J]. Journal of Geophysics and Engineering,2018,15(5):1991−2000.
[18] 罗腾腾,徐基祥,孙夕平. 应用迭代收缩高分辨率Radon变换的绕射波分离与成像方法[J]. 石油地球物理勘探,2021,56(2):313−322.
LUO Tengteng,XU Jixiang,SUN Xiping. Diffraction wave separation and imaging based on high–resolution Radon transform on an iterative model shrinking approach[J]. Oil Geophysical Prospecting,2021,56(2):313−322.
[19] CHEN Wei,LIU Xingye,SAAD O M,et al. 3–D seismic diffraction separation and imaging using the local rank–reduction method[J]. IEEE Transactions on Geoscience and Remote Sensing,2021,60:4507110.
[20] LIN Peng,PENG Suping,ZHAO Jingtao,et al. Diffraction separation and imaging using multichannel singular–spectrum analysis[J]. Geophysics,2020,85(1):V11−V24.
[21] 霍伟光,曹静杰,陈雪,等. 基于Cook距离的阻尼多道奇异谱分析分离绕射波[J]. 石油地球物理勘探,2024,59(4):771−781.
HUO Weiguang,CAO Jingjie,CHEN Xue,et al. Damped multichannel singular spectrum analysis for diffraction separation based on the Cook–distance[J]. Oil Geophysical Prospecting,2024,59(4):771−781.
[22] LIU Yulin,LIN Peng,PENG Suping,et al. Weighted multichannel singular spectrum analysis for diffraction amplitude preservation[J]. Geophysics,2025,90(3):V191−V203.
[23] LOWNEY B,LOKMER I,O’BRIEN G S. Multi–domain diffraction identification:A supervised deep learning technique for seismic diffraction classification[J]. Computers & Geosciences,2021,155:104845.
[24] ZWARTJES P,YOO J. Common–offset domain reflection–diffraction separation with deep learning[J]. Geophysical Prospecting,2023,71(1):85−101.
[25] SHENG Tongjie,ZHAO Jingtao. Separation and imaging of diffractions using a dilated convolutional neural network[J]. Geophysics,2022,87(3):S117−S127.
[26] SUN Jiaxing,YANG Jidong,LI Zhenchun,et al. Reflection and diffraction separation in the dip–angle common–image gathers using convolutional neural network[J]. Geophysics,2023,88(1):WA281−WA291.
[27] YU Siwei,MA Jianwei. Complex variational mode decomposition for slop–preserving denoising[J]. IEEE Transactions on Geoscience and Remote Sensing,2018,56(1):586−597.
[28] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531−544.
[29] LIN Peng,ZHAO Jingtao,PENG Suping,et al. Diffraction separation by variational mode decomposition[J]. Geophysical Prospecting,2021,69(5):1070−1085.
[30] 宋启航. 基于模态分解技术的地震信号去噪方法研究[D]. 大庆:东北石油大学,2020.
SONG Qihang. Research on seismic signal denoising method based on modal decomposition technology[D]. Daqing:Northeast Petroleum University,2020.
[31] 江星星,宋秋昱,杜贵府,等. 变分模式分解方法研究与应用综述[J]. 仪器仪表学报,2023,44(1):55−73.
JIANG Xingxing,SONG Qiuyu,DU Guifu,et al. Review on research and application of variational mode decomposition[J]. Chinese Journal of Scientific Instrument,2023,44(1):55−73.
[32] 王肖,周怀来,王元君,等. 基于MP与GA–VMD结合的地震资料去噪方法研究[J]. 物探化探计算技术,2023,45(2):156−168.
WANG Xiao,ZHOU Huailai,WANG Yuanjun,et al. Research on seismic data denoising method based on MP and GA–VMD[J]. Computing Techniques for Geophysical and Geochemical Exploration,2023,45(2):156−168.
[33] MIRJALILI S,MIRJALILI S M,LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software,2014,69:46−61.
[34] HAO Peng,SOBHANI B. Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model[J]. International Journal of Hydrogen Energy,2021,46(73):36454−36465.
[35] RAD P B,SCHWARZ B,GAJEWSKI D,et al. Common–reflection–surface–based prestack diffraction separation and imaging[J]. Geophysics,2018,83(1):S47−S55.
[36] DECKER L,MERZLIKIN D,FOMEL S. Diffraction imaging and time–migration velocity analysis using oriented velocity continuation[J]. Geophysics,2017,82(2):U25−U35.
[37] LI Chuangjian,ZHAO Jingtao,PENG Suping,et al. Prestack diffraction separation in the common virtual source gather[J]. Geophysics,2021,86(2):S113−S124.
[38] FOMEL S. Time–migration velocity analysis by velocity continuation[J]. Geophysics,2003,68(5):1662−1672.
[39] FOMEL S,LANDA E,TANER M T. Poststack velocity analysis by separation and imaging of seismic diffractions[J]. Geophysics,2007,72(6):U89−U94.
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