A reflection waveform inversion method based on de-migrated data of characteristic reflection layer

doi:10.3969/j.issn.2096-1693.2026.01.006

Abstract

Abstract:

Reflection waveform inversion (RWI) exploits reflected-wave information in seismic data to update the deep background velocity model. By alternately inverting for the migration and tomographic components, RWI not only improves the accuracy of deep velocity model updates but also alleviates the cycle-skipping problem to a certain degree. However, RWI generally requires seismic data with a high signal-to-noise ratio (SNR) and has so far achieved its most successful applications in marine environments. In contrast, land seismic data are often degraded by poor receiver coupling, rugged topography, environmental noise, and strong surface-wave interference, making it difficult to acquire continuous and high-SNR reflection waveforms, which severely limits the applicability of RWI to land data. To address these challenges, this study employs Kirchhoff pre-stack time migration to identify characteristic reflection layer and extract their corresponding common-image gathers (CIGs). The extracted events are then reverse migrated to reconstruct reflected-wave data with enhanced SNR. The reconstructed data are subsequently incorporated into RWI and validated using both synthetic and field data examples. The results demonstrate that the proposed method significantly improves the accuracy of deep background velocity model updates. Furthermore, the strong consistency between the migrated images and the corresponding CIGs confirms the reliability and effectiveness of the reconstructed reflection data for RWI applications. Overall, this method offers a new feasible solution for applying RWI to land seismic data.

Key words: full waveform inversion, reflection waveform inversion, Kirchhoff migration and de-migration, characteristic horizon picking, horizon-based reflection data

摘要：

反射波形反演(Reflection waveform inversion，RWI)主要利用地震数据中的反射波信息，对地下深部背景速度模型进行更新。其通过交替迭代更新偏移分量与层析分量，不仅能够更好的对深层速度模型进行更新，还可在一定程度上能够缓解周期跳跃问题。然而，RWI对数据的信噪比要求较高，目前主要在海洋资料中取得了较好的应用效果。相比之下，陆地数据受检波器耦合、起伏地表、环境噪声及面波干扰等因素影响，往往难以获取波形连续且高信噪比的反射波信息，严重制约了RWI 方法在陆地数据中的应用。为解决上述问题，本文基于Kirchhoff叠前时间偏移，拾取特征层及其对应的共成像道集(Common-image gathers，CIGs)，并通过反偏移生成高信噪比的反射波数据。随后，将该数据引入RWI，并在合成数据与实际数据中进行了验证。结果表明，该方法能够有效改善深部背景速度模型的更新精度；同时，偏移成像结果和CIGs的对比进一步证明了所构建反射波数据在 RWI 中的适用性与有效性。总体而言，该方法为RWI在陆地数据中的应用提供了一种新的可行性方案。

关键词: 全波形反演, 反射波形反演, Kirchhoff偏移和反偏移, 特征层位拾取, 特征层位反射波数据

CLC Number:

LIU Nengchao, WANG Shangxu, WU Bo, YAO Gang. A reflection waveform inversion method based on de-migrated data of characteristic reflection layer[J]. Petroleum Science Bulletin, 2026, 11(1): 54-65.

刘能超, 王尚旭, 吴博, 姚刚. 基于特征层位反偏移数据的反射波形反演方法[J]. 石油科学通报, 2026, 11(1): 54-65.

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URL: https://sykxtb.cup.edu.cn/EN/10.3969/j.issn.2096-1693.2026.01.006

https://sykxtb.cup.edu.cn/EN/Y2026/V11/I1/54

Figures/Tables 16

Fig. 1 A flowchart of RWI method for characteristic reflection layer

Fig. 2 Schematic diagram of RWI wavefield

Fig. 3 Schematic diagram of characteristic horizons creation (a) characteristic horizon layers on profile and CIG’s location; (b) characteristic horizon picking on CIGs; (c) reflection coefficient from CIGs;(d) wavelet;(e) new CIGs

Fig. 4 (a) True model and (b) initial model

Fig. 5 Time domain (a) initial CIGs; (b) characteristic horizon picking; (c) new CIG

Fig. 6 Shot gather (a) original data and (b) de-migration data

Fig. 7 Comparison of velocity model and Kirchhoff depth migration (a) initial model, (c) RWI model using noise attenuation data after de-migration, (f) RWI model using characteristic horizon data after de-migration; (b), (d), (f) are the depth migration corresponding to (a), (c), (e)

Fig. 8 Depth CIGs (a) and (d) corresponding to true model; (b) and (c) corresponding to initial model; (c) and (f) corresponding to RWI model

Fig. 9 Poland onshore data (a) schematic diagram of observation map; (b) surface elevation map

Fig. 10 Shot gather (a) original data; (b) after noise attenuation

Fig. 11 Time domain (a) RMS velocity model; (b) Kirchhoff pre-stack migration

Fig. 12 Kirchhoff pre-stack migration profile and character-istic horizon picking

Fig. 13 Time domain (a) CIGs; (b) characteristic horizon picking; (c) new CIGs

Fig. 14 Shot gather data(a) after noise attenuation; (b) after de-migration; (c) after de-migration using characteristic horizon data

Fig. 15 Comparison of velocity model and Kirchhoff depth migration (a) initial model,(c) RWI model using noise attenuation data after de-migration, (f) RWI model using characteristic horizon data after de-migration; (b),(d),(f) are the depth migrations corresponding to (a),(c),(e)

Fig. 16 Comparison of depth CIGs located at 6.2 km、7.9 km and 10.9 km: (a)~(c) corresponding to the initial model(fig.15a); (d)~(f) corresponding to the inversion model (fig.15b); (g)~(i) corresponding to the inversion model (fig.15c)

References 37

[1]	周嘉欣, 杨锴, 邵炜栋. 慢度平方三角网格模型VTI介质二维QP波各向异性立体层析反演[J]. 地球物理学报, 2021, 64(11): 4134-4149. doi: 10.6038/cjg2021L0473
	[ZHOU J X, YANG K, SHAO W D. 2D QP anisotropic stereo-tomography for VTI media in triangulated model[J]. Chinese Journal of Geophysics, 2021, 64(11): 4134-4149.]
[2]	TAVAKOLI F B, OPERTO S, RIBODETTI A, et al. Matrix-free anisotropic slope tomography: Theory and application[J]. Geophysics, 2019, 84(1): R21-R43. doi: 10.1190/GEO2018-0112.1
[3]	FU L Y. Interpretative seismic imaging with the wavenumber-structure monitoring of velocity models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5924714.
[4]	MESSUD J, GUILLAUME P, LAMBARÉ G. On tomography velocity uncertainty in relation with structural imaging[J]. Geophysics, 2021, 86(4): U89-U107. doi: 10.1190/geo2020-0603.1 URL
[5]	HU S H, ZHAO Z Y, SEN M K. Bayesian full-waveform inversion using adaptive Markov chain Monte Carlo methods[J]. Geophysics, 2025, 90(5): R325-R344. doi: 10.1190/geo2024-0671.1 URL
[6]	杨顶辉, 董兴朋, 黄建东, 等. 高分辨率全波形地震成像研究——进展、挑战与展望[J]. 中国科学: 地球科学, 2025, 55(2): 319-347.
	[YANG D H, DONG X P, HUANG J D, et al. High-resolution full waveform seismic imaging: Progresses, challenges, and prospects[J]. Science China Earth Sciences, 2025, 68(2): 315-342.] doi: 10.1007/s11430-024-1498-0
[7]	HUANG X Q, LIU Y Z. An efficient elastic full-waveform inversion of multiple parameters with ocean-bottom seismometer data[J]. Geophysical Prospecting, 2024, 72(6): 2123-2147.
[8]	ADWANI A, DANILOUCHKINE M, AL-SIYABI Q, et al. Onshore model building using elastic full-waveform inversion on surface and body waves: A case study from Sultanate of Oman[J]. Geophysics, 2022, 87(6): R413-R424.
[9]	KEATING S, ZUNINO A, FICHTNER A. Full-waveform ambient noise inversion on finite domains[J]. Geophysical Journal International, 2025, 242(2): ggaf229. doi: 10.1093/gji/ggaf229 URL
[10]	WU B, YAO G, ZHENG Q Q, et al. Improved elastic full-waveform inversion of ocean bottom node data[J]. Geophysical Prospecting, 2025, 73(3): 923-941. doi: 10.1111/gpr.v73.3 URL
[11]	QU Y M, DONG S H, ZHONG T M, et al. Cross-correlation reflection waveform inversion based on a weighted norm of the time-shift obtained by dynamic image warping[J]. Geophysical Prospecting, 2025, 73(3): 910-922. doi: 10.1111/gpr.v73.3 URL
[12]	LI J L, ZHU J G, QU Y M, et al. Elastic full-waveform inversion based on the double-cross-shaped discrete flux-corrected transport[J]. Geophysical Prospecting, 2024, 72(2): 468-483. doi: 10.1111/gpr.v72.2 URL
[13]	CHEN F, PETER D, RAVASI M. Cycle-skipping mitigation using misfit measurements based on differentiable dynamic time warping[J]. Geophysics, 2022, 87(4): R325-R335. doi: 10.1190/geo2021-0598.1 URL
[14]	YAO G, DA SILVA N V, WARNER M, et al. Tackling cycle skipping in full-waveform inversion with intermediate data[J]. Geophysics, 2019, 84(3): R411-R427.
[15]	ZHANG P, WU R S, HAN L G, et al. Elastic direct envelope inversion based on wave mode decomposition for multi-parameter reconstruction of strong-scattering media[J]. Petroleum Science, 2022, 19(5): 2046-2063. doi: 10.1016/j.petsci.2022.05.007 URL
[16]	LI X, YAO G, NIU F L, et al. Waveform inversion of seismic first arrivals acquired on irregular surface[J]. Geophysics, 2022, 87(3): R291-R304. doi: 10.1190/geo2021-0097.1 URL
[17]	YAO G, DA SILVA N V, WU D. Reflection-waveform inversion regularized with structure-oriented smoothing shaping[J]. Pure and Applied Geophysics, 2019, 176(12): 5315-5335. doi: 10.1007/s00024-019-02265-6
[18]	姚刚, 吴迪. 反射波全波形反演[J]. 中国科学: 地球科学, 2017, 47(10): 1220-1232.
	[YAO G, WU D. Reflection full waveform inversion[J]. Science China Earth Sciences, 2017, 47(10): 1220-1232.]
[19]	HADDEN S, PRATT R G, SMITHYMAN B. Anisotropic full-waveform inversion of crosshole seismic data: A vertical symmetry axis field data application[J]. Geophysics, 2019, 84(1): B15-B32. doi: 10.1190/geo2017-0790.1 URL
[20]	XU S, WANG D, CHEN F, et al. Inversion on reflected seismic wave[C]// SEG Technical Program Expanded Abstracts, Las Vegas, 2012.
[21]	王伟奇, 李振春, 孙小东, 等. 地震处理领域面波压制技术现状及发展趋势[J]. 地球物理学进展, 2022, 37(3): 1178-1188.
	[WANG W Q, LI Z C, SUN X D, et al. Current situation and development trend of surface wave suppression technology in seismic processing[J]. Progress in Geophysics, 2022, 37(3): 1178-1188.]
[22]	赵锐锐, 陈新哲, 向平奥, 等. 地震资料层间多次波压制方法研究进展[J]. 石油物探, 2024, 63(6): 1163-1176.
	[ZHAO R R, CHEN X Z, XIANG P A, et al. Research progress of interbed multiples suppression[J]. Geophysical Prospecting for Petroleum, 2024, 63(6): 1163-1176.]
[23]	SUN H R, LIU E L, ZHANG F, et al. Seismic shear wave noise suppression and application to well tie[J]. Journal of Geophysics and Engineering, 2024, 21(6): 1619-1635. doi: 10.1093/jge/gxae091 URL
[24]	马继涛. 基于三维抛物线Radon变换的多次波压制算法对比分析[J]. 石油物探, 2022, 61(3): 444-453, 511. doi: 10.3969/j.issn.1000-1441.2022.03.006
	[MA J T. Comparison and analysis of multiple attenuation algorithms based on three-dimensional parabolic Radon transform[J]. Geophysical Prospecting for Petroleum, 2022, 61(3): 444-453, 511.] doi: 10.3969/j.issn.1000-1441.2022.03.006
[25]	曹中林, 李振, 陈爱萍, 等. 基于Cadzow滤波法压制线性干扰[J]. 物探与化探, 2015, 39(4): 842-847.
	[CAO Z L, LI Z, CHEN A P, et al. Linear interference suppressed by Cadzow filter method[J]. Geophysical and Geochemical Exploration, 2015, 39(4): 842-847.]
[26]	曹静杰, 高康富, 许银坡, 等. 基于一种注意力机制U-Net的地震数据去噪方法[J]. 石油地球物理勘探, 2024, 59(4): 724-735.
	[CAO J J, GAO K F, XU Y P, et al. Seismic data de-noising method based on an attention mechanism U-Net[J]. Oil Geophysical Prospecting, 2024, 59(4): 724-735.]
[27]	HUANG W L, WANG R Q. Random noise attenuation by planar mathematical morphological filtering[J]. Geophysics, 2018, 83(1): V11-V25. doi: 10.1190/geo2017-0288.1 URL
[28]	蔡希玲. 声波和强能量干扰的分频自适应检测与压制方法[J]. 石油地球物理勘探, 1999, 34(4): 373-380.
	[CAI X L. An effective method to suppress acoustic wave and high energy noise frequency-divisionally and adaptively[J]. Oil Geophysical Prospecting, 1999, 34(4): 373-380.]
[29]	陈超群, 戴海涛, 高秦, 等. 复杂地表条件下地震资料一致性处理方法研究与应用[J]. 物探与化探, 2023, 47(4): 954-964.
	[CHEN C Q, DAI H T, GAO Q, et al. A processing method for seismic data consistency under complex surface conditions and its applications[J]. Geophysical and Geochemical Exploration, 2023, 47(4): 954-964.]
[30]	MA Y, CHEN F, CHENG C, et al. Reflection full waveform inversion and its application to land data[C]// SEG Technical Program Expanded Abstracts, San Antonio, Texas, 2019.
[31]	LI Z C, QU Y M. Research progress on seismic imaging technology[J]. Petroleum Science, 2022, 19(1): 128-146.
[32]	WU Z D, ALKHALIFAH T. Simultaneous inversion of the background velocity and the perturbation in full-waveform inversion[J]. Geophysics, 2015, 80(6): R317-R329.
[33]	ZHOU W, BROSSIER R, OPERTO S, et al. Full waveform inversion of diving & reflected waves for velocity model building with impedance inversion based on scale separation[J]. Geophysical Journal International, 2015, 202(3): 1535-1554. doi: 10.1093/gji/ggv228 URL
[34]	WILLIAM A, SCHNEIDE R. Integral formulation for migration in two and three dimensions[J]. Geophysics, 1978, 43: 49-76. doi: 10.1190/1.1440828 URL
[35]	BLEISTEIN N, GRAY S H. From the Hagedoorn imaging technique to Kirchhoff migration and inversion[J]. Geophysical Prospecting, 2001, 49(6): 629-643.
[36]	杨庆道, 蒋兵, 岳玉波, 等. 基于Kirchhoff偏移/反偏移的随机噪声压制方法[J]. 石油物探, 2013, 52(5): 530-536, 444. doi: 10.3969/j.issn.10001441.2013.05.013
	[YANG Q D, JIANG B, YUE Y B, et al. A random noise suppression method based on data mapping by Kirchhoff migration/demigration[J]. Geophysical Prospecting for Petroleum, 2013, 52(5): 530-536, 444.]
[37]	LIU N C, ZHANG F, WANG S X, et al. Joint velocity building and imaging of compressional and shear waves: Application to gas cloud imaging[J]. Journal of Geophysics and Engineering, 2025, 22(6): 1926-1941. doi: 10.1093/jge/gxaf043 URL

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