Research on microseismic interferometric location method based on the instantaneous phase
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摘要:
波形叠加定位法具有自动性和抗噪性等优点, 已被广泛应用于微地震事件定位.当该类方法采用特征函数变换原始波形以克服初至极性变化影响时, 会降低成像分辨率.而将相位加权叠加法应用于微地震成像时, 虽然提高了成像分辨率, 但并未考虑复杂震源机制对定位的影响.为了校正波形极性, 并提高干涉成像法的分辨率和压制噪声的能力, 本文重新组合了原始互相关波形的振幅和瞬时相位信息, 提出了互相关相位加权成像法(CCPW).通过数值算例对比了互相关相位加权成像法(CCPW)、基于绝对值干涉成像法(AII)和基于长短时窗能量比干涉成像法(SLII), 讨论了不同方法在抗噪性和成像分辨率等方面的性能.理论测试结果表明: 新方法能够校正波形极性变化, 具有较强的抗噪性, 提高了成像分辨率.最后将三种方法应用于地面监测的实际矿震数据中, 验证了新方法的有效性.
Abstract:The waveform stacking-based location methods have been widely used for the microseismic event location due to their automaticity and noise-resistance. The imaging resolution of these methods will be reduced when characteristic functions for original waveform transformation are used to overcome the effect of first-motion polarity changes. Although the imaging resolution is improved, the phase-weighted stack method does not consider the effect of complex source mechanisms on location when it is applied to microseismic imaging. In order to correct the waveform polarity, and to further improve the resolution and noise-resistance ability of the interferometric imaging method, this work proposes a cross-correlation phase-weighted (CCPW) imaging method which recombines the amplitude and instantaneous phase information of the original cross-correlation waveform. The performance of the cross-correlation phase-weighted (CCPW) imaging method, absolute value-based interferometric imaging (AII), and short-term average to long-term average ratio (STA/LTA)-based interferometric imaging (SLII) is investigated by numerical examples, in terms of noise-resistance, imaging resolution and so on. The numerical tests indicate that the new method can address the first-motion polarity changes, have strong anti-noise performance, and improve the imaging resolution. Finally, these three methods are applied to field mining-induced seismicity monitored by surface stations and the effectiveness of the new method is verified.
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图 3
最终叠加值随指数v的取值变化示意图
Figure 3.
Sketch of the final stacked value changing with the value of exponent v
图 4
互相关波形偏移叠加结果对比
Figure 4.
Comparison of cross-correlation waveforms migration stacking results
图 5
无噪声Vx分量的干涉成像结果
Figure 5.
Interferometric imaging results of the Vx component without noise
图 6
(a) 速度模型和震源-检波器阵列示意图；震源S1添加噪声后的(b)Vx分量波形和(c)Vz分量波形
Figure 6.
(a) Sketch of velocity model and source-receiver array; (b) Vx component waveforms and (c) Vz component waveforms of S1 with noise
图 7
不同噪声条件下的100次蒙特卡洛模拟的定位结果
Figure 7.
Location results of Monte Carlo simulation by 100 times under different noise conditions
图 8
三种方法的成像剖面中过震源位置的归一化成像值曲线对比
Figure 8.
Comparison of normalized imaging value curves passing through the source location in imaging profiles of three methods
图 9
三种方法在六种速度模型下的定位结果对比(品红色五角星为正确震源位置)
Figure 9.
Comparison of location results of three methods under six velocity models (The magenta pentagram represents the correct source location)
图 10
三种方法利用不同震相组合的成像结果
Figure 10.
Imaging results of three methods using different seismic phase combinations
图 11
三种方法对100个强微地震事件在(a)E-N平面和(b)E-Z平面内的定位结果和100个弱微地震事件在(c)E-N平面和(d)E-Z平面内的定位结果
Figure 11.
Location results of 100 strong microseismic events in (a) E-N plane and (b) E-Z plane and 100 weak microseismic events in (c) E-N plane and (d) E-Z plane obtained by three methods
图 12
三种方法对序号为10的弱微地震事件的成像结果(白色五角星为走时反演定位结果)
Figure 12.
Imaging results of No.10 weak microseismic event obtained by three methods (The white pentagram represents the source location obtained by travel time inversion)
图 13
(a) 序号为90的强微地震事件的Vz分量波形图；互相关相位加权成像法分别对红色实线框和红色虚线框内波形得到的关于P波成像的垂直剖面图(b)和(c)(白色五角星为走时反演定位结果).
Figure 13.
(a) Waveforms of Vz component of the No. 90 strong microseismic event; (b) and (c) Vertical imaging profiles for P-waves in the red solid line border and red dashed line border obtained by CCPW (The white pentagram represents the source location obtained by travel time inversion).
图 14
序号为90的强微地震事件的(a)Vx分量；(b) 经STA/LTA处理后的水平分量；(c) Vz分量；(d) 经STA/LTA处理后的Vz分量
Figure 14.
(a) Vx component; (b) horizontal component processed by STA/LTA; (c) Vz component; (d) Vz component processed by STA/LTA of the No.90 strong microseismic event
图 15
序号为10的弱微地震事件的(a) Vx分量；(b) 经STA/LTA处理后的水平分量；(c) Vz分量；(d) 经STA/LTA处理后的Vz分量
Figure 15.
(a) Vx component; (b) horizontal component processed by STA/LTA; (c) Vz component and (d) Vz component processed by STA/LTA of the No.10 weak microseismic event
表 1
互相关相位加权成像法极性校正原理
Table 1.
Polarity correlation principle of CCPW
原始波形振幅 对应逐采样点归一化解析信号中的值 二者的加权乘积 a exp(iϕ) a·exp(iϕ) －a exp(i(ϕ+π)) －a·exp(i(ϕ+π))=a·exp(iϕ) 
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表 2
二维均匀模型参数
Table 2.
Parameters of two-dimensional homogeneous model
参数名称/单位 数值 网格点数 201×201 网格间距/m 2.5 时间采样间隔/ms 0.5 纵波速度/(m·s-1) 3000 纵、横波速度比 1.67 密度/(kg·m-3) 3000 震源频率/Hz 60 检波器个数 51
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表 3
六种速度模型下三种方法定位结果的绝对误差对比(单位：m)
Table 3.
Absolute error comparison of location results of three methods under six-velocity models (Unit: m)
速度误差 互相关相位加权成像法 基于绝对值干涉成像法 基于STA/LTA干涉成像法 +20% 0, -40 0, -42.5 0, -42.5 +15% 0, -32.5 0, -32.5 0, -35 +10% 0, -22.5 0, -22.5 0, -27.5 -10% 0, +30 0, +30 0, +22.5 -15% 0, +47.5 0, +47.5 0, +35 -20% 0, +67.5 0, +67.5 0, +55
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表 4
三种方法性能总结
Table 4.
Summary of the performance of three methods
评估指标 互相关相位加权成像法 基于绝对值干涉成像法 基于STA/LTA干涉成像法 抗噪性 好 较差 最差 分辨率 好 较差 最差 受速度模型误差影响程度 大 大 小 P波定位时受续至S波等相干波影响程度 大 大 小
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Besk ardes G D, Hole J A, Wang K, et al. 2018. A comparison of earthquake backprojection imaging methods for dense local arrays. Geophysical Journal International, 212(3): 1986-2002, doi: 10.1093/gji/ggx520.
Cao L. 2015. Study on the source location and focal mechanism inversion[Ph. D. thesis] (in Chinese). Beijing: University of Chinese Academy of Sciences.
Cesca S, Grigoli F. 2015. Chapter two-full waveform seismological advances for microseismic monitoring. Advances in Geophysics, 56: 169-228, doi: 10.1016/bs.agph.2014.12.002.
Gajewski D, Anikiev D, Kashtan B, et al. 2007. Localization of seismic events by diffraction stacking. //77th Ann. Internat Mtg., Soc. Expi. Geophys. . Expanded Abstracts, 1287-1291, doi: 10.1190/1.2792738.
Gharti H N, Oye V, Roth M, et al. 2010. Automated microearthquake location using envelope stacking and robust global optimization. Geophysics, 75(4): MA27-MA46, doi: 10.1190/1.3432784.
Grigoli F, Cesca S, Vassallo M, et al. 2013. Automated seismic event location by travel-time stacking: an application to mining induced seismicity. Seismological Research Letters, 84(4): 666-677, doi: 10.1785/0220120191.
Kao H, Shan S J. 2004. The source-scanning algorithm: Mapping the distribution of seismic sources in time and space. Geophysical Journal International, 157(2): 589-594, doi: 10.1111/j.1365-246X.2004.02276.x.
Li L, Chen H, Wang X M. 2015. Weighted-elastic-wave interferometric imaging of microseismic source location. Applied Geophysics, 12(2): 221-234, doi: 10.1007/s11770-015-0479-z.
Li L, Becker D, Chen H, et al. 2018. A systematic analysis of correlation-based seismic location methods. Geophysical Journal International, 212(1): 659-678, doi: 10.1093/gji/ggx436.
Li L, Tan J Q, Wood D A, et al. 2019. A review of the current status of induced seismicity monitoring for hydraulic fracturing in unconventional tight oil and gas reservoirs. Fuel, 242: 195-210, doi: 10.1016/j.fuel.2019.01.026.
Li L, Tan J Q, Schwarz B, et al. 2020. Recent advances and challenges of waveform-based seismic location methods at multiple scales. Reviews of Geophysics, 58(1): e2019RG000667, doi: 10.1029/2019RG000667.
Li Z, Chang X, Yao Z X, et al. 2019. Fracture monitoring and reservoir evaluation by micro-seismic method. Chinese Journal of Geophysics (in Chinese), 62(2): 707-719, doi: 10.6038/cjg2018L0729.
Li Z C, Sheng G Q, Wang W B, et al. 2014. Time-reverse microseismic hypocenter location with interferometric imaging condition based on surface and downhole multi-components. Oil Geophysical Prospecting (in Chinese), 49(4): 661-666, 671, doi: 10.13810/j.cnki.issn.1000-7210.2014.04.006.
Maxwell S C. 2009. Microseismic location uncertainty. CSEG Recorder, 34(4): 41-46.
Maxwell S C. 2014. Microseismic Imaging of Hydraulic Fracturing. Tulsa: Society of Exploration Geophysicists.
Poiata N, Satriano C, Vilotte J P, et al. 2016. Multiband array detection and location of seismic sources recorded by dense seismic networks. Geophysical Journal International, 205(3): 1548-1573, doi: 10.1093/gji/ggw071.
Schimmel M, Paulssen H. 1997. Noise reduction and detection of weak, coherent signals through phase-weighted stacks. Geophysical Journal International, 130(2): 497-505, doi: 10.1111/j.1365-246X.1997.tb05664.x.
Schuster G T, Yu J, Sheng J, et al. 2004. Interferometric/daylight seismic imaging. Geophysical Journal International, 157(2): 838-852, doi: 10.1111/j.1365-246X.2004.02251.x.
Staněk F, Anikiev D, Valenta J, et al. 2015. Semblance for microseismic event detection. Geophysical Journal International, 201(3): 1362- 1369, doi: 10.1093/gji/ggv070.
Tan F Z. 2019. Improving Beamforming-based Methodologies for Seismological Analysis [Ph. D. thesis]. Canada: University of Victoria.
Tian X, Zhang W, Zhang J. 2016. Cross double-difference inversion for microseismic event location using data from a single monitoring well. Geophysics, 81(5): KS183-KS194, doi: 10.1190/geo2016-0198.1.
Tian X, Zhang W, Zhang J. 2017. Cross double-difference inversion for simultaneous velocity model update and microseismic event location. Geophysical Prospecting, 65(S1): 259-273, doi: 10.1111/1365-2478.12556.
Tian X, Zhang X, Zhang H, et al. 2020. Full-interferometry imaging method for microseismic location. Chinese Journal of Geophysics (in Chinese), 63(8): 3105-3115, doi: 10.6038/cjg2020M0634.
Trojanowski J, Eisner L. 2016. Comparison of migration-based location and detection methods for microseismic events. Geophysical Prospecting, 65(1): 47-63, doi: 10.1111/1365-2478.12366.
Wang W B, Zhou Y Q, Chun L. 2012. Characteristics of source localization by seismic emission tomgraphy for surface based on microseismic monitoring. Journal of China University of Petroleum (in Chinese), 36(5): 45-50, 55, doi: 10.3969/j.issn.1673-5005.2012.05.008.
Wang X X, Ding Z F, Ma Y L. 2017. Rapidly tracking the rupture energy center of the MW7.9 Nepal earthquake by using nonlinear array stacking method. Chinese Journal of Geophysics (in Chinese), 60(1): 142-150, doi: 10.6038/cjg20170112.
Wu S J, Wang Y B, Zheng Y K, et al. 2018. Microseismic source locations with deconvolution migration. Geophysical Journal International, 212(3): 2088-2115, doi: 10.1093/gji/ggx518.
Xu L S, Du H L, Yan C, et al. 2013. A method for determination of earthquake hypocentroid: time-reversal imaging technique I——Principle and numerical tests. Chinese Journal of Geophysics (in Chinese), 56(4): 1190-1206, doi: 10.6038/cjg20130414.
曹雷. 2015. 地震定位及震源机制解反演研究[博士论文]. 北京: 中国科学院大学.
李振春, 盛冠群, 王维波等. 2014. 井地联合观测多分量微地震逆时干涉定位. 石油地球物理勘探, 49(4): 661-666, 671, doi: 10.13810/j.cnki.issn.1000-7210.2014.04.006.
李政, 常旭, 姚振兴等. 2019. 微地震方法的裂缝监测与储层评价. 地球物理学报, 62(2): 707-719, doi: 10.6038/cjg2018L0729. http://www.igg-journals.cn/article/doi/10.6038/cjg2018L0729
田宵, 张雄, 张华等. 2020. 全干涉成像的微地震定位方法研究. 地球物理学报, 63(8): 3105-3115, doi: 10.6038/cjg2020M0634. http://www.igg-journals.cn/article/doi/10.6038/cjg2020M0634
王维波, 周瑶琪, 春兰. 2012. 地面微地震监测SET震源定位特性研究. 中国石油大学学报(自然科学版), 36(5): 45-50, 55, doi: 10.3969/j.issn.1673-5005.2012.05.008.
王晓欣, 丁志峰, 马延路. 2017. 利用非线性台阵叠加方法快速追踪2015年4月25日尼泊尔MW7.9地震破裂能量中心运动轨迹. 地球物理学报, 60(1): 142-150, doi: 10.6038/cjg20170112.
许力生, 杜海林, 严川等. 2013. 一种确定震源中心的方法: 逆时成像技术(一)——原理与数值实验. 地球物理学报, 56(4): 1190-1206, doi: 10.6038/cjg20130414. http://www.igg-journals.cn/article/doi/10.6038/cjg20130414
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