基于Context模型的Shearlet变换地面微地震随机噪声压制
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摘要
地面微地震监测采集到的微地震信号通常能量微弱,信噪比低,如何提高微震数据的信噪比是数据处理的难题.Shearlet变换是一种新型的多尺度几何分析方法,具有敏感的方向性和较强的稀疏表示特性,能起到很好的随机噪声压制效果.由于地面微震数据的有效信号大多被淹没在噪声中,基于传统阈值的Shearlet变换(the traditional threshold-based Shearlet transform TST)只考虑到尺度或方向的阈值,在去噪过程中会过度扼制有效信号系数,造成有效信号能量损失.因而,本文建立Context模型,得到基于Context模型的Shearlet变换(the Context-model-based Shearlet transform CMST)方法,改进传统Shearlet阈值方法的不足.我们通过所建立的Context模型将能量相近的各方向系数划分为同一组,并分组估计阈值,分别处理各部分系数,达到微弱同相轴有效恢复的目的.通过TST及CMST的模拟实验与实际地面微震记录处理结果对比可知,本文方法在低信噪比条件下比对比方法更加有效地恢复地面微震数据的微弱信号,随机噪声压制效果明显,在-10dB条件下,提升信噪比18.3741dB.
Microseismic monitoring technique is an important means of unconventional oil and gas exploration.Surface microseismic monitoring is becoming more common in analyzing unconventional hydrocarbon resources.However,the low signal-to-noise ratio(SNR)greatly hinders the realization of the identification and location of microseismic events.Therefore,it is vital to eliminate noise and improve the SNR in the processing of surface microseismic data.Shearlet transform is a new kind of multiscale geometric analysis method,which has the characteristics of sensitive direction and strong sparse representation,can reduce the random noise well by properly setting a threshold to the shearlet coefficients.In the process of surface microseismic random noise suppression,it is difficult for the traditional threshold-based Shearlet transform(TST)method to break throughthe limitation of recovering the weak signal.The TST method only considering the scale or direction threshold will excessively curb effective signal coefficient in the denoising process,causing energy losses.In this paper,through the establishment of Context model,we get the Context-modelbased Shearlet transform(CMST)method to improve the traditional method.Due to the wide numerical range of the shearlet coefficients of scale layers in all directions,unified threshold for the coefficients of shrinkage is not the optimal choice.The CMST method establishes the Context model to group the scale layers coefficients in all directions,considering the position threshold of the shearlet coefficients,and sets up the reasonable threshold of each group.Through several simulation experiments and processing of actual surface microseismic data,we can conclude that the proposed method is more effective in restoring the weak signal and enhancing the SNR by18.3741 dB under the condition of raw SNR of-10 dB.
Microseismic monitoring technique is an important means of unconventional oil and gas exploration.Surface microseismic monitoring is becoming more common in analyzing unconventional hydrocarbon resources.However,the low signal-to-noise ratio(SNR)greatly hinders the realization of the identification and location of microseismic events.Therefore,it is vital to eliminate noise and improve the SNR in the processing of surface microseismic data.Shearlet transform is a new kind of multiscale geometric analysis method,which has the characteristics of sensitive direction and strong sparse representation,can reduce the random noise well by properly setting a threshold to the shearlet coefficients.In the process of surface microseismic random noise suppression,it is difficult for the traditional threshold-based Shearlet transform(TST)method to break throughthe limitation of recovering the weak signal.The TST method only considering the scale or direction threshold will excessively curb effective signal coefficient in the denoising process,causing energy losses.In this paper,through the establishment of Context model,we get the Context-modelbased Shearlet transform(CMST)method to improve the traditional method.Due to the wide numerical range of the shearlet coefficients of scale layers in all directions,unified threshold for the coefficients of shrinkage is not the optimal choice.The CMST method establishes the Context model to group the scale layers coefficients in all directions,considering the position threshold of the shearlet coefficients,and sets up the reasonable threshold of each group.Through several simulation experiments and processing of actual surface microseismic data,we can conclude that the proposed method is more effective in restoring the weak signal and enhancing the SNR by18.3741 dB under the condition of raw SNR of-10 dB.
引文
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Chang S G,Yu B,Vetterli M.2000.Spatially adaptive wavelet thresholding with context modeling for image denoising.IEEETransactions on Image Processing,9(9):1522-1531.
Do M N,Vetterli M.2005.The contourlet transform:An efficient directional multiresolution image representation.IEEE Transactions on Image Processing,14(12):2091-2106.
Donoho D L,Duncan M R.2000.Digital Curvelet transform:Strategy,implementation,and experiments.Proceedings of SPIE,4056:12-30.
Du Z J,Foulger G R,Mao W J.2000.Noise reduction for broadband,three-component seismograms using data-adaptive polarization filters.Geophysical Journal International,141(3):820-828.
Easley G R,Labate D,Colonna F.2009.Shearlet-based total variation diffusion for denoising.IEEE Transactions on Image Processing,18(2):260-268.
Guo K H,Labate D.2007a.Optimally sparse multidimensional representation using Shearlets.SIAM Journal on Mathematical Analysis,39(1):298-318.
Guo K H,Labate D.2007b.Sparse shearlet representation of Fourier integral operators.Electronic Research Announcements in Mathematical Sciences,14(29):7-19.
Guo K H,Labate D.2013.The construction of smooth parseval frames of Shearlets.Mathematical Modelling of Natural Phenomena,8(1):82-105.
Kittipoom P,Kutyniok G,Lim W Q.2012.Construction of compactly supported shearlet frames.Constructive Approximation,35(1):21-72.
Kushnir A,Varypaev A,Dricker I,et al.2014.Passive surface microseismic monitoring as a statistical problem:Location of weak microseismic signals in the presence of strongly correlated noise.Geophysical Prospecting,62(4):819-833.
Lin H B,Li Y,Xu X C.2011.Segmenting time-frequency peak filtering method to attenuation of seismic random noise.Chinese Journal of Geophysics(in Chinese),54(5):1358-1366,doi:10.3969/j.issn.0001-5733.2011.05.025.
Maxwell S C,Urbancic T I.2001.The role of passive microseismic monitoring in the instrumented oil field.The Leading Edge,20(6):636-639.
Maxwell S C,Rutledge J,Jones R,et al.2010.Petroleum reservoir characterization using downhole microseismic monitoring.Geophysics,75(5):A129-A137.
Mousavi S M,Langston C A.2016.Hybrid seismic denoising using higher-order statistics and improved wavelet block thresholding.Bulletin of the Seismological Society of America,106(4):1380-1393.
Shemeta J,Anderson P.2010.It′s a matter of size:Magnitude and moment estimates for microseismic data.The Leading Edge,29(3):296-302.
Song W Q,Chen Z D,Mao Z H.2008.Hydro-Fracturing Break Microseismic Monitoring Technology(in Chinese).Dongying:China University of Petroleum Press.
Xu H B,Li S L,Chen J J.2012.A study on method of signal denoising based on wavelet transform for micro-seismicity monitoring in large-scale rockmass structures.Acta Seismologica Sinica(in Chinese),34(1):85-96.
林红波,李月,徐学纯.2011.压制地震勘探随机噪声的分段时频峰值滤波方法.地球物理学报,54(5):1358-1366,doi:10.3969/j.issn.0001-5733.2011.05.025.
宋维琪,陈泽东,毛中华.2008.水力压裂裂缝微地震监测技术.东营:中国石油大学出版社.
徐宏斌,李庶林,陈际经.2012.基于小波变换的大尺度岩体结构微震监测信号去噪方法研究.地震学报,34(1):85-96.
Chang S G,Yu B,Vetterli M.2000.Spatially adaptive wavelet thresholding with context modeling for image denoising.IEEETransactions on Image Processing,9(9):1522-1531.
Do M N,Vetterli M.2005.The contourlet transform:An efficient directional multiresolution image representation.IEEE Transactions on Image Processing,14(12):2091-2106.
Donoho D L,Duncan M R.2000.Digital Curvelet transform:Strategy,implementation,and experiments.Proceedings of SPIE,4056:12-30.
Du Z J,Foulger G R,Mao W J.2000.Noise reduction for broadband,three-component seismograms using data-adaptive polarization filters.Geophysical Journal International,141(3):820-828.
Easley G R,Labate D,Colonna F.2009.Shearlet-based total variation diffusion for denoising.IEEE Transactions on Image Processing,18(2):260-268.
Guo K H,Labate D.2007a.Optimally sparse multidimensional representation using Shearlets.SIAM Journal on Mathematical Analysis,39(1):298-318.
Guo K H,Labate D.2007b.Sparse shearlet representation of Fourier integral operators.Electronic Research Announcements in Mathematical Sciences,14(29):7-19.
Guo K H,Labate D.2013.The construction of smooth parseval frames of Shearlets.Mathematical Modelling of Natural Phenomena,8(1):82-105.
Kittipoom P,Kutyniok G,Lim W Q.2012.Construction of compactly supported shearlet frames.Constructive Approximation,35(1):21-72.
Kushnir A,Varypaev A,Dricker I,et al.2014.Passive surface microseismic monitoring as a statistical problem:Location of weak microseismic signals in the presence of strongly correlated noise.Geophysical Prospecting,62(4):819-833.
Lin H B,Li Y,Xu X C.2011.Segmenting time-frequency peak filtering method to attenuation of seismic random noise.Chinese Journal of Geophysics(in Chinese),54(5):1358-1366,doi:10.3969/j.issn.0001-5733.2011.05.025.
Maxwell S C,Urbancic T I.2001.The role of passive microseismic monitoring in the instrumented oil field.The Leading Edge,20(6):636-639.
Maxwell S C,Rutledge J,Jones R,et al.2010.Petroleum reservoir characterization using downhole microseismic monitoring.Geophysics,75(5):A129-A137.
Mousavi S M,Langston C A.2016.Hybrid seismic denoising using higher-order statistics and improved wavelet block thresholding.Bulletin of the Seismological Society of America,106(4):1380-1393.
Shemeta J,Anderson P.2010.It′s a matter of size:Magnitude and moment estimates for microseismic data.The Leading Edge,29(3):296-302.
Song W Q,Chen Z D,Mao Z H.2008.Hydro-Fracturing Break Microseismic Monitoring Technology(in Chinese).Dongying:China University of Petroleum Press.
Xu H B,Li S L,Chen J J.2012.A study on method of signal denoising based on wavelet transform for micro-seismicity monitoring in large-scale rockmass structures.Acta Seismologica Sinica(in Chinese),34(1):85-96.
林红波,李月,徐学纯.2011.压制地震勘探随机噪声的分段时频峰值滤波方法.地球物理学报,54(5):1358-1366,doi:10.3969/j.issn.0001-5733.2011.05.025.
宋维琪,陈泽东,毛中华.2008.水力压裂裂缝微地震监测技术.东营:中国石油大学出版社.
徐宏斌,李庶林,陈际经.2012.基于小波变换的大尺度岩体结构微震监测信号去噪方法研究.地震学报,34(1):85-96.