Attenuation characteristics and wave impedance inversion of depth-domain nonstationary seismic data

Authors

MA Ming, Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Xiamen 361000, China; Technology Innovation Center for Geothermal & Hot Dry Rock Exploration and Development, Ministry of Natural Resources, Shijiazhuang 050061, ChinaFollow
MA Feng, Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Xiamen 361000, China; Technology Innovation Center for Geothermal & Hot Dry Rock Exploration and Development, Ministry of Natural Resources, Shijiazhuang 050061, China

Abstract

Background The efficient application of depth migration methods has enabled the extensive implementation of depth-domain seismic data interpretation. Previous studies on the impacts of time-to-depth conversion and velocity models merely determine that an increase in depth corresponds to reduced dominant wavenumber of seismic waves and waveform stretching, without considering the changes in amplitude and phase. Objective This study aims to accurately characterize the waveforms of nonstationary seismic signals in the depth domain and enhance the impedance inversion accuracy. Methods First, based on energy dissipation and frequency dispersion effects during the propagation of seismic waves, this study investigated the complex mapping relationship between nonstationary seismic reflected waves and formation impedance and introduced the Q model that reflected the absorption attenuation of strata for seismic waves. Nonstationary convolution formula for seismic reflection data were proposed to simultaneously describe the amplitude attenuation, phase distortion, and decrease in the primary wavenumber of seismic waves. Accordingly, the impedance inversion equation was established under spatial constraints. Second, this study estimated the Q model for strata using deep learning technology. The multi-head self-attention mechanism was introduced into the network structure, allowing for the extraction of accurate attenuation characteristics of depth-domain seismic signals. The assumption of a known Q model in the conventional inversion process was abandoned. Instead, some synthetic data were employed for network training and validation, ensuring the convenient implementation of the estimation method. Third, the depth-varying seismic wavelets were calculated using Q values yielded from the network, and the multi-trace impedance inversion method based on the sparsity constraint from the l_p norm was employed. Ultimately, the high-resolution absolute impedance data volume in the depth domain was determined. Results and Conclusions Validation using the Pluto model demonstrated that the Q model and nonstationary inversion achieved using deep learning technology yielded a relative error in impedance of 13.7%, suggesting significantly improved inversion accuracy compared to the results of conventional stationary inversion (48.2%). Tests using field seismic data from an exploration area of the Jinzhong coalfield indicate that deep-domain nonstationary seismic inversion technology can capture the physical property parameters of subsurface media more intuitively and accurately. The impedance determined using the inversion technology showed a high similarity of 0.9488 to the impedance curve determined based on log data, thus avoiding instability caused by multiple processing steps such as inverse Q filtering and recursive inversion. The results of this study provide depth-domain stratigraphic information for reference in subsequent seismic interpretation.

Keywords

depth-domain nonstationary seismic data, Q estimation, impedance inversion, deep learning, lp norm

DOI

10.12363/issn.1001-1986.25.01.0043

Recommended Citation

M M. (2025) "Attenuation characteristics and wave impedance inversion of depth-domain nonstationary seismic data," Coal Geology & Exploration: Vol. 53: Iss. 9, Article 16.
DOI: 10.12363/issn.1001-1986.25.01.0043
Available at: https://cge.researchcommons.org/journal/vol53/iss9/16

Reference

[1] 曹思远，孙耀光，陈思远. 地震勘探高分辨率资料处理的挑战与对策[J]. 煤田地质与勘探，2023，51(1)：277−288.

CAO Siyuan，SUN Yaoguang，CHEN Siyuan. Challenges and solutions to high–resolution data processing for seismic exploration[J]. Coal Geology & Exploration，2023，51(1)：277−288.

[2] 李亚林. 超深层油气地震勘探技术进展[J]. 石油地球物理勘探，2024，59(4)：915−924.

LI Yalin. Ultra-deep oil and gas reservoir seismic prospecting technologies progress[J]. Oil Geophysical Pros-pecting，2024，59(4)：915−924.

[3] 常锁亮，张生，刘晶，等. 薄互层条件下围岩变化对煤层反射波的影响研究[J]. 煤田地质与勘探，2021，49(5)：220−229.

CHANG Suoliang，ZHANG Sheng，LIU Jing，et al. Influence of surrounding rock changes on the coal seam reflected wave under thin interbed condition[J]. Coal Geology & Exploration，2021，49(5)：220−229.

[4] 罗红梅，王长江，刘书会，等. 深度域高精度井震动态匹配方法[J]. 石油地球物理勘探，2018，53(5)：997−1005.

LUO Hongmei，WANG Changjiang，LIU Shuhui，et al. Well–to–seismic calibration in the depth domain using dynamic depth warping[J]. Oil Geophysical Prospecting，2018，53(5)：997−1005.

[5] MARGRAVE G F. Theory of nonstationary linear filtering in the Fourier domain with application to time–variant filtering[J]. Geophysics，1998，63(1)：244−259.

[6] 张静，杨勤林，王天琦. 深度域地震反演方法探索[J]. 石油地球物理勘探，2010，45(增刊1)：114−116.

ZHANG Jing，YANG Qinlin，WANG Tianqi. Probing for seismic inversion in depth domain[J]. Oil Geophysical Prospecting，2010，45(Sup.1)：114−116.

[7] CAMERON M，FOMEL S，SETHIAN J. Time–to–depth conversion and seismic velocity estimation using time–migration velocity[J]. Geophysics，2008，73(5)：VE205−VE210.

[8] DIX C H. Seismic velocities from surface measurements[J]. Geophysics，1955，20(1)：68−86.

[9] LI Siwei，FOMEL S. A robust approach to time–to–depth conversion and interval velocity estimation from time migration in the presence of lateral velocity variations[J]. Geophysical Prospecting，2015，63(2)：315−337.

[10] 高远，王琦，董守华，等. 煤田三维地震叠前深度偏移技术及其应用[J]. 煤田地质与勘探，2011，39(3)：71−73.

GAO Yuan，WANG Qi，DONG Shouhua，et al. The technology of coal 3D prestack depth migration and its applications[J]. Coal Geology & Exploration，2011，39(3)：71−73.

[11] ZHANG Rui，DENG Zhiwen. A depth variant seismic wavelet extraction method for inversion of poststack depth–domain seismic data[J]. Geophysics，2018，83(6)：R569−R579.

[12] 李闯，韩令贺，杨哲，等. 深层—超深层海相碳酸盐岩地震勘探技术发展与攻关方向[J]. 石油地球物理勘探，2024，59(2)：368−379.

LI Chuang，HAN Linghe，YANG Zhe，et al. Development and tackling directions of seismic exploration technology for deep and ultra-deep marine carbonate rocks[J]. Oil Geophysical Prospecting，2024，59(2)：368−379.

[13] ETGEN J T，KUMAR C. What really is the difference between time and depth migration? A tutorial[C]//SEG Technical Program Expanded Abstracts 2012. Society of Exploration Geophysicists，2012：1–5.

[14] LIN Tengfei，ZHANG Bo，GUO Shiguang，et al. Spectral decomposition of time– versus depth–migrated data[C]//SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists，2013：1384–1388.

[15] MA Ming，ZHANG Rui，YUAN Sanyi. Multichannel impedance inversion for nonstationary seismic data based on the modified alternating direction method of multipliers[J]. Geophysics，2019，84(1)：A1−A6.

[16] YUAN Sanyi，WANG Shangxu，MA Ming，et al. Sparse Bayesian learning–based time–variant deconvolution[J]. IEEE Transactions on Geoscience and Remote Sensing，2017，55(11)：6182−6194.

[17] SENGUPTA M，ZHANG Houzhu，ZHAO Yang，et al. Direct depth–domain Bayesian amplitude–variation–with–offset inversion[J]. Geophysics，2021，86(5)：M167−M176.

[18] SUN Chengyu，CAI Ruiqian，YAO Zhen’an. A wavelet extraction method of attenuation media for direct acoustic impedance inversion in depth domain[J]. Applied Sciences，2024，14(6)：2478.

[19] ZHANG Rui，DENG Zhiwen. Depth–domain angle and depth variant seismic wavelets extraction for prestack seismic inversion[J]. Geophysics，2023，88(1)：R1−R10.

[20] WANG Yanghua. A stable and efficient approach of inverse Q filtering[J]. Geophysics，2002，67(2)：657−663.

[21] WANG Yanghua. Quantifying the effectiveness of stabilized inverse Q filtering[J]. Geophysics，2003，68(1)：337−345.

[22] 柴新涛. 非稳态地震数据反射系数反演方法研究与应用[D]. 北京：中国石油大学(北京)，2016.

CHAI Xintao. Reflectivity inversion for nonstationary seismic data：Theory and application[D]. Beijing：China University of Petroleum (Beijing)，2016.

[23] 马铭. 自适应评价空间关系的稀疏贝叶斯学习反射系数反演方法研究[D]. 北京：中国石油大学(北京)，2018.

MA Ming. Research of reflectivity inversion with automatic spatially correlated evaluation based on sparse Bayesian learning[D]. Beijing：China University of Petroleum (Beijing)，2018.

[24] VAN DER BAAN M，JUTTEN C. Neural networks in geophysical applications[J]. Geophysics，2000，65(4)：1032−1047.

[25] AKI K，RICHARDS P G. Quantitative seismology[M]. Sausalito：University Science Books，2002.

[26] LIU Ze，LIN Yutong，CAO Yue，et al. Swin transformer：Hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal：IEEE，2021：9992–10002.

[27] LIU Ze，HU Han，LIN Yutong，et al. Swin transformer V2：Scaling up capacity and resolution[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans：IEEE，2022：11999–12009.

[28] CHEN L C，ZHU Yukun，PAPANDREOU G，et al. Encoder–decoder with atrous separable convolution for semantic image segmentation[C]//Computer Vision–ECCV 2018. Cham：Springer，2018：833–851.

[29] GRAY S H，MARFURT K J. Migration from topography：Improving the near–surface image[J]. Canadian Journal of Exploration Geophysics，1995，31(1/2)：18−24.

[30] ETGEN J，REGONE C. Strike shooting，dip shooting，widepatch shooting：Does prestack depth migration care? A model study[C]//SEG Technical Program Expanded Abstracts 1998. Society of Exploration Geophysicists，1998：66–69.

[31] BILLETTE F J，BRANDSBERG–DAHL S. The 2004 BP velocity benchmark[C]//67th EAGE Conference & Exhibition. Madrid：European Association of Geoscientists & Engineers，2005.

[32] SHAH H. The 2007 BP anisotropic velocity–analysis benchmark[C]. EAGE Workshop，2008.

[33] MARTIN G S，WILEY R，MARFURT K J. Marmousi2：An elastic upgrade for marmousi[J]. The Leading Edge，2006，25(2)：156−166.

[34] ROSA A L R，ULRYCH T J. Processing via spectral modeling[J]. Geophysics，1991，56(8)：1244−1251.