基于特征层位反偏移数据的反射波形反演方法

doi:10.3969/j.issn.2096-1693.2026.01.006

石油科学通报 ›› 2026, Vol. 11 ›› Issue (1): 54-65. doi: 10.3969/j.issn.2096-1693.2026.01.006

基于特征层位反偏移数据的反射波形反演方法

刘能超¹^,²(), 王尚旭¹^,²^,^*(), 吴博³, 姚刚¹^,^*()

1 中国石油大学(北京) 油气资源与工程全国重点实验室，北京 102249
2 中国石油大学(北京) 地球物理学院，北京 102249
3 中石化石油物探技术研究院有限公司，南京 211103

收稿日期:2025-10-27 修回日期:2026-01-15 出版日期:2026-02-15 发布日期:2026-02-12
通讯作者: * 王尚旭(1962年—)，博士，教授，从事岩石物理实验、地震物理模型试验、地震信号分析与反演等方面的研究，wangsx@cup.edu.cn。
姚刚(1983年—)，博士，教授，主要研究最小二乘逆时偏移，全波形反演理论及其工业化应用，人工智能在地震勘探领域的应用等方面的研究，yaogang@cup.edu.cn。
作者简介:刘能超(1979年—)，博士研究生，从事地震数据处理、全波形反演建模方面的研究，lnengchao@qq.com。
基金资助:
国家自然科学基金“全波形地震成像与弹性参数同步反演理论及核心算法研究”(U23B20159)

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

LIU Nengchao¹^,²(), WANG Shangxu¹^,²^,^*(), WU Bo³, YAO Gang¹^,^*()

1 State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, China
2 College of Geophysics, China University of Petroleum, Beijing 102249, China
3 Sinopec Geophysical Research Institute, Nanjing 211103, China

Received:2025-10-27 Revised:2026-01-15 Online:2026-02-15 Published:2026-02-12
Contact: *wangsx@cup.edu.cn;yaogang@cup.edu.cn

摘要/Abstract

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

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

中图分类号:

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.

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.

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

图/表 16

图1 特征层RWI反演流程图

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

图2 RWI的波场示意图
红色五角星表示震源，蓝色三角表示检波器，水平虚线表示界面

Fig. 2 Schematic diagram of RWI wavefield

图3 特征层生成示意图(a)剖面特征层及道集位置；(b)CIGs上的特征层拾取；(c)CIGs对应的反射系数；(d)子波；(e)新的CIGs

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

图4 (a)真实模型和(b)初始速度模型

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

图5 时间域(a)原始CIGs；(b)特征层位拾取；(c)新生成的CIGs

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

图6 单炮记录(a)原始数据和(b)反偏移数据

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

图7 速度模型和Kirchhoff深度域偏移结果对比(a)真实模型；(c)初始模型；(e)RWI模型；(b), (d), (f)是(a), (c), (e)所对应的偏移结果

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)

图8 深度域(a)和(d)为真实模型的CIGs道集；(b)和(e)初始模型的CIGs道集；(c)和(f)是RWI反演模型的CIGs道集

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

图9 波兰陆地数据(a)观测系统示意图；(b)地表高程图

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

图10 单炮记录

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

图11 时间域(a)均方根速度模型；(b)Kirchhoff叠前偏移成像

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

图12 Kirchhoff叠前偏移成像剖面及特征层位拾取

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

图13 时间域(a)CIGs；(b) 特征层拾取；(c) 新生成的CIGs

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

图14 单炮数据

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

图15 速度模型和Kirchhoff深度域偏移结果对比(a)初始模型；(c)采用去噪数据(图14b所示)的观测数据进行RWI的反演结果；(e)采用特征层反偏移数据(图14c所示)的观测数据进行RWI的反演结果；(b), (d), (f)是(a), (c), (e)所对应的偏移结果

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)

图16 深度域CIGs对比：(a)~(c)分别为在距离为6.2 km、7.9 km和10.9 km处初始模型(图15a)的CIGs道集；(d)~(f)分别为对应的常规去噪反偏移数据反演速度(图15c)的CIGs道集；(g)~(i)分别为对应的特征层反偏移数据反演速度(图15e)的CIGs道集

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)

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