Review of progress in passive seismic reflection exploration
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摘要: 被动源反射地震勘探作为一种低成本且环保的方法，能够获得比被动源面波勘探方法更高分辨率的反射地震剖面，近年来越来越受到人们的关注. 被动源反射成像技术面临着诸多挑战，例如背景噪声场主要受控于面波能量，反射体波信号弱且不易提取；地下实际震源数量有限且分布不均匀，地震干涉重构的虚拟炮集信噪比低；大量检波器长时间观测的海量数据计算和存储瓶颈等. 随着便携式节点地震仪和高性能计算的快速发展，被动源反射地震勘探不仅在方法研究方面取得不少进展，而且在地下矿产勘查、碳封存动态监测等方面都有实际应用. 本文首先简要回顾地震干涉法的发展历程和如何利用不同地震干涉方法构建虚拟炮集，然后较为详细地介绍从面波主导的背景噪声记录中甄别和提取有效反射信号，以及被动源原始数据预处理、虚拟炮集多次波压制和直接偏移成像等方面的进展，最后通过几个实例了解被动源反射地震勘探的应用现状，并对将来的研究前景进行简单展望.Abstract: Seismic exploration is a key method for the study of underground structures. Active seismic surveys can acquire high signal-to-noise ratio reflection data; however, the operation of active seismic surveys is complicated, and the exploration cost is high. Passive seismic exploration is another type of seismic exploration, which requires no active human build source but utilizes the natural noise recorded by seismograph stations or geophones. As a low-cost and environmentally friendly method, passive seismic reflection exploration can be used to create higher resolution seismic profiles than the surface wave method and plays an increasingly important role in underground mineral exploration, dynamic monitoring of carbon storage, and urban underground structure detection. However, passive source reflection imaging technology faces several challenges. For example, because ambient noise is mainly controlled by surface wave energy, the reflected body wave signal can be weak and difficult to extract. Actual underground sources are limited in number and are unevenly distributed, which leads to artifacts in the virtual shot gathers reconstructed by seismic interference. There are also constraints of massive data computation and storage for long-term observation by a large number of geophones. With the rapid development of portable node geophones and high-performance computing, passive seismic reflection exploration has achieved considerable progress in both method research and practical applications in recent years. This paper briefly reviews the history of seismic interferometry and the construction of virtual shot gathers using various seismic interferometry methods and then introduces in detail how the reflection signals from ambient noise records dominated by surface waves are identified and extracted. We discuss the identification and extraction of weak body wave reflection signals based on various characteristics of surface and body waves, such as differences in signal-to-noise ratio, velocity, and azimuth angle. We then focus on the processing of passive source reflection data, including the beginning of raw data preprocessing, virtual shot gather static correction, coherent noise suppression, multiple suppression, velocity analysis, and direct migration imaging. We also introduce examples of passive seismic reflection applications, including CO2 storage site monitoring, metal mining, and coal mine underground structure research. Finally, we give an outlook for research prospects in passive seismic reflection exploration.
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图 1 地震干涉示意图（修改自Schuster, 2009）
Figure 1. Seismic interferometry schematic diagram (modified from Schuster, 2009)
图 2 封闭区域模型的地震干涉（Wapenaar and Fokkema, 2006）.
$\mathrm{格}\mathrm{林}\mathrm{函}\mathrm{数}\widehat{G}\left({x}_{{\rm{A}}},{x}_{{\rm{B}}},\omega \right)$ 可由观测点${x}_{{\rm{A}}}\mathrm{、}{x}_{{\rm{B}}}$ 和沿着边界$\partial D$ 的震源$ x $ 积分的互相关获得Figure 2. Seismic interferometry model enclosed by boundary (Wapenaar and Fokkema, 2006) Green's function
$ \widehat{G}\left({x}_{A},{x}_{B},\omega \right) $ can be obtained by cross-correlating the observations at$ {x}_{A} \; \mathrm{a}\mathrm{n}\mathrm{d }\; {x}_{B} $ and integrating along source coordinate$ x $ at$\partial D$ 
图 3 虚源法试验（曹辉等，2012）. （ａ）地表激发、井中接收；（ｂ）虚源构建后的观测系统，消除了近地表复杂条件对波场的透射影响
Figure 3. Virtual source method experiments (Cao et al., 2012) . (a) The sources are fired on the surface, and the receivers are located in the borehole; (b) Acquisition geometry after virtual source construction, removing the effects of the complicated near surface
图 4 不同地震干涉方法计算的模拟数据虚拟炮集（修改自Liu et al., 2020）. （ａ）互相关；（ｂ）反褶积；（ｃ）互相干
Figure 4. Virtual shot gathers using noise-free data as input; generated by (a) cross-correlation, (b) deconvolution, and (c) cross-coherence (modified from Liu et al., 2020)
图 5 不同地震干涉方法计算的实际数据虚拟炮集（方捷等，2022）. （ａ）互相关；（ｂ）反褶积；（ｃ）互相干
Figure 5. Virtual shot gathers using low S/N data as input; generated by (a) cross-correlation, (b) deconvolution, and (c) cross-coherence (Fang et al., 2022)
图 6 不规则分布震源的地震干涉数值计算示例（修改自Wapenaar et al., 2008）. （a）横向均匀的水平层状模型，不规则分布震源依次发出瞬态信号；（b）单个地下震源的地震记录响应；（c）互相关重构炮集（黑色点折线）与源在x=0处的直接模拟炮集（红线）对比；（d）多维反褶积炮集（黑色点折线）
Figure 6. Numerical simulation of seismic interferometry with irregular sources (modified from Wapenaar et al., 2008). (a) Configuration with a horizontally layered target below a homogeneous overburden and a free surface. The irregularly distributed sources below the target emit transient signals sequentially. (b) Response of one of the sources. (c) Interferometry from cross-correlation (red traces) compared with the directly modeled response of a source at x=0 (black dashed traces). (d) Interferometry from multidimensional deconvolution
图 7 不同震源参数模拟的地震干涉结果（修改自Thorbecke and Draganov, 2011）. （a）地下震源随机分布的10×4 km数值模型；（b）地表中点激发的主动源单炮；（c-e）分别用8000、1000、50个随机位置地下噪声震源激发构建的被动源虚拟炮集；（f）1000个随机位置雷克子波震源激发的重构虚拟炮集；（g） 1000个固定2700 m位置深度的雷克子波震源同时激发的重构虚拟炮集
Figure 7. SI results from modeling by different sources (modified from Thorbecke and Draganov, 2011). (a) The 10×4 km model with sources positioned at random locations, visible as black dots. (b) A directly modeled reference result for an actual source at x=0 m. SI results for noise signature sources for varying numbers of (c) 8000, (d) 1000, and (e) 50. (f) SI results from 1000 sources using the Ricker wavelet in random locations. (g) SI results by 1000 sources using the Ricker wavelet at z=2700 m; the sources are started simultaneously
图 8 体波提取前后的地震干涉模拟结果（修改自Vidal et al., 2014）. （a）利用所有记录重构的虚拟炮集；（b）提取体波后的重构虚拟炮集；（c）主动源激发单炮
Figure 8. SI result for selection of body wave retrieval (modified from Vidal et al., 2014). (a) Retrieved virtual common-source panel with all recorded data. (b) Retrieved virtual common-source panel after the selection of body wave retrieval. (c) Directly modeled reflection response
图 9 被动源信号不同频率成分的功率密度谱特征（刘国峰等，2021）. （a）面波能量占优；（b）体波能量占优
Figure 9. Power density spectrum characteristics for different frequency components of the passive seismic signal (modified from Liu et al., 2021). (a) Energy dominated by surface wave; (b) Energy dominated by body wave
图 10 频率域计算信噪比参考图（张军华等，2009）
Figure 10. Spectrum for the signal-to-noise ratio calculation in the frequency domain (Zhang et al., 2009)
图 11 频率域分离计算后的虚拟炮集记录（刘国峰等，2021）. （a）以面波为主的虚拟炮集记录；（b）分离面波后突出体波的虚拟炮集记录，红色箭头为反射波信号
Figure 11. Virtual shot gathers after frequency domain separation calculation (Liu et al., 2021). (a) Shot gather with surface wave as the main energy; (b) Shot gather with body wave as the main energy after surface wave separation. Red arrows indicate the reflection wave
图 12 倾斜叠加照明分析示意图（修改自Vidal et al., 2014）. （a）弹性地下模型，黄色倒三角地表检波器，黑点代表地下震源；（b）浅源数据（灰色星）重构的虚拟炮集；（c）虚拟炮集图（b）对应的慢度；（d-e）深源数据（青色星）的虚拟炮集和慢度，绿线表示面波和体波慢度的区分界限；（f）地下深源数据倾斜叠加照明分析的慢度分布图
Figure 12. Slant-stack illumination diagnosis (modified from Vidal et al., 2014) . (a) Elastic subsurface model with yellow triangles as receivers and black dots as sources. (b) The virtual source retrieved from the shallow source is marked by the grey star. Red lines indicate slownesses. Green lines are pre-defined limits between body and surface wave slownesses in the virtual source function. (c) Slowness representation of the virtual source function from (b). (d) As in (b), but for the source represented by the cyan star in (a). (e) As in (c), but from (d). (f) Illumination diagnosis, consisting of the results for the correlated common source panels from sources in (a) (black dots), with the panel from (b) (grey star) discarded, and the panel from (d) (cyan star) included
图 13 三维观测系统更好区分面波和体波示意图（修改自Chamarczuk et al., 2019）. （a）五个检波器 X1 至 X5 记录的来自地表震源S1和地下震源 S2 的直达走时. 两源同时激发，且假定介质传播速度恒定5 km/s. （b）由五个检波器形成的十字排列观测系统. （c）由三条检波线组成的三维观测系统
Figure 13. The diagram of event discrimination from surface- or body-waves (modified from Chamarczuk et al., 2019). (a) Direct arrivals from sources S1 and S2 recorded at five receivers X1 to X5 forming a cross-shaped array. We assume a constant propagation velocity of 5 km/s. (b) Configuration of the cross-shaped array formed by the five receivers. (c) 3D survey formed by the three receiver lines
图 14 波束成形的方向性分析（修改自Cheraghi et al., 2017）. 正北方向为0°，角度沿逆时针方向增加
Figure 14. Directional Beam-forming analysis (modified from Cheraghi et al., 2017). The north had an azimuth of 0°, which increased to the west (i.e., counterclockwise)
图 15 不同小时段的最大波束功率及其视速度和方位角（修改自Cheraghi et al., 2015）
Figure 15. Calculated beamformers for the maximum beam power of each hourly panel and its apparent velocity and azimuth (modified from Cheraghi et al., 2015)
图 16 加拿大Lalor矿区R141线的倾角时差校正叠加剖面（修改自Cheraghi et al., 2015）. （a）主动源剖面；（b）被动源剖面
Figure 16. DMO stacked section along line R141 in Lalor deposit, Canada (modified from Cheraghi et al., 2015). (a) Active-source 3D survey and (b) passive interferometry survey
图 17 时间域强振幅控制示意图（方捷等，2022）
Figure 17. Strong amplitude control in the time domain (Fang et al., 2022)
图 18 频率域强振幅控制示意图（修改自Girard and Shragge, 2019）. （a）频率域强振幅控制前（蓝色）和后（红色）的时域信号；（b）频率域强振幅控制前（蓝色）和后（红色）的部分频谱
Figure 18. Strong amplitude control in the frequency domain (modified from Girard and Shragge, 2019). (a) Time-domain trace before (blue) and after (red) debursting. (b) Partial spectrum before (blue) and after (red) debursting
图 19 互相关+叠加得到的以共偏移距（1.0 km）排列的虚拟道集（修改自Girard and Shragge, 2019）. （a）原始数据；（b）窗口选择数据；（c）强振幅压制数据
Figure 19. Single extracted cross-correlation + stack trace at a virtual source-receiver offset of 1.0 km (modified from Girard and Shragge, 2019). (a) Raw data. (b) After data selection. (c) After time and frequency debursting
图 20 基于聚焦变换和SRME三维稀疏反演的被动源数据去噪试验（修改自Wang et al., 2021）. （a）数值试验速度模型；（b）随机震源采用的子波；（c）震源分布位置；（d）互相关生成的被动源虚拟炮集；（e）应用该方法处理的虚拟炮集；（f）SRME处理后的主动源炮集
Figure 20. Numerical tests of noise removal via focal-denoising closed-loop SRME based on the 3D sparse inversion of passive data (modified from Wang et al., 2021). (a) Model for simulating noise-source data and (b) wavelets of the random sources. (c) Source location. Virtual shot gathers from the noise-source data using (d) cross-correlation and (e) the proposed method. (f) Active-source data using SRME
图 21 被动源反射信号速度分析和局部叠加（刘国峰等，2021）
Figure 21. Reflection velocity analysis and stacked local passive data (Liu et al., 2021)
图 22 被动源地震数据直接偏移的数值试验（修改自Cai et al., 2019）. （a）Marmousi模型，震源分布在左下方的黑框红框；（b）随机截取的一段4 s的被动源记录；（c）基于全波场分离归一化的被动源数据直接逆时偏移成像
Figure 22. Numerical tests for the direct migration of passive data (modified from Cai et al., 2019). (a) Marmousi model with the passive data generated from source locations marked by black and red boxes. (b) Example of a passive seismic record with a length of 4 s. (c) The direct migration image by full wavefields decomposition normalized reverse time migration
图 23 加拿大Lalor湖附近矿区主、被动源水平切片和垂直剖面（修改自Girard and Shragge, 2020）. （a）被动源数据的直接偏移成像；（b）主动源数据的偏移结果
Figure 23. Comparison of the horizontal slice and vertical section of 3D active and passive surveys in Lalor deposit, Canada (modified from Girard and Shragge, 2020). (a) Passive direct migration stack and (b) the active-source migration volume
图 24 加拿大Saskatchewan的CO2封存场所的被动源研究（修改自Cheraghi et al., 2017）. （a）被动源剖面（红色为注入井的合成记录）；（b）井的合成记录（子波从被动源数据提取）；（c）主动源数据剖面；（d）深时转换的P波速度测井记录. LC：下科罗拉多页岩；VF：Vanguard组砂岩；BK：Bakken页岩；PC：前寒武纪岩石
Figure 24. Passive surveys of aquistore CO2 storage site Saskatchewan, Canada. (modified from Cheraghi et al., 2017). (a) Passive section (red curve is the log-based synthetic seismogram). (b) Synthetic seismogram generated from a wavelet extracted using the passive data convolved with borehole log-based reflectivity. (c) Active-source cube. (d) Depth-to-time converted P-wave velocity log. LC, VF, BK, and PC represent the Lower Colorado shale, Vanguard Formation, Bakken shale, and Precambrian, respectively
图 25 CO2封存场所的L1和L2线不同时段的被动源地震成像（修改自Cheraghi et al., 2017）
Figure 25. Passive seismic imaging for different time periods at the CO2 storage site along lines L1 and L2 (modified from Cheraghi et al., 2017)
图 26 主、被动源三维共炮集数据对比（修改自Chamarczuk et al., 2022）. （a）主动源炮集；（b）30天记录重构的被动源虚拟炮集；（c）10天记录重构的被动源虚拟炮. VSG：虚拟炮集
Figure 26. Comparison of 3D common-source gathers using active and passive data (modified from Chamarczuk et al., 2022). (a) Active-shot gather, (b) VSGs obtained using 30 d of noise, and (c) VSGs obtained using 10 d of noise. VSG:Virture Shot Gathers
图 27 芬兰Kylylahti结晶岩矿集区主、被动源叠后偏移剖面（修改自Chamarczuk et al., 2022）. （a） 主动源剖面；（b）主动源剖面叠合地质解释；（c）被动源剖面叠合地质解释KAL沉积（蓝色）；OUM岩体（绿色）；S/MS矿化半矿化岩体（红色）
Figure 27. Comparison of post-stack migrated sections obtained from active and passive surveys in Kylylahti polymetallic mine, Finland (modified from Chamarczuk et al., 2022). (a) Active survey; (b) Active survey with the geological model background; (c) Passive survey with the geological model background. Geology in the background is color-coded as follows: Kaleva Sedimentary Belt (KAL): blue; semi-massive to massive sulfide (S/MS) mineralization: red; Outokumpu ultra-mafics (OUM): green
图 28 不同检波线的偏移剖面（修改自Ning et al., 2021）. 黑色虚线表明煤层成像位置
Figure 28. Migration sections of receiver lines (modified from Ning et al., 2021). The black dashed lines show the coal seams imaged using ambient-noise data
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