基于单道奇异值分解的微地震资料去噪方法
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摘要
奇异值分解(SVD)是一种利用地震资料的相关性进行去噪的有效方法。但对于信号较弱、信噪比极低的微地震资料,传统SVD方法处理效果较差。针对单道微地震记录噪声周期性较强的特点,提出了一种基于单道SVD的去噪方法。首先利用单道微地震记录来构建分解矩阵,然后对矩阵进行奇异值分解,通过对奇异值分布规律的分析,选取适当的奇异值实现矩阵的重构,最后通过SVD反变换得到重构信号,从而达到去除噪声、突出有效信号的目的。实践表明,该方法能有效去除微地震记录中的噪声,为微地震事件的正确识别与震源的准确定位提供强有力的前提保障。
The singular value decomposition(SVD) was an effective denoising method using the correlation of seismic data.However,because microseismic signals were weak,and signal to noise ratio of micro-seismic data was extremely low,it was difficult to obtain a satisfactory processing result by using conventional SVD method.According to strong cyclical characteristics of noise to single-channel microseismic record,a denoising method based on single-channel SVD was proposed.It uses a single-channel seismic records to build the decomposing matrix,and then selects appropriate singular values to rebuild the matrix through analyzing the distribution law of the singular values of matrix,the purpose of removing noise and highlighting effective signals is achieved through rebuilding signals by using SVD inverse transform.The practice shows that this method can effectively remove the noise in the microseismic records.It provides a strong guarantee for identifying microseismic events correctly and locating sources exactly.
The singular value decomposition(SVD) was an effective denoising method using the correlation of seismic data.However,because microseismic signals were weak,and signal to noise ratio of micro-seismic data was extremely low,it was difficult to obtain a satisfactory processing result by using conventional SVD method.According to strong cyclical characteristics of noise to single-channel microseismic record,a denoising method based on single-channel SVD was proposed.It uses a single-channel seismic records to build the decomposing matrix,and then selects appropriate singular values to rebuild the matrix through analyzing the distribution law of the singular values of matrix,the purpose of removing noise and highlighting effective signals is achieved through rebuilding signals by using SVD inverse transform.The practice shows that this method can effectively remove the noise in the microseismic records.It provides a strong guarantee for identifying microseismic events correctly and locating sources exactly.
引文
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[10]沈鸿雁,李庆春.频域奇异值分解(SVD)地震波场去噪[J].石油地球物理勘探,2010,25(1):185～189.
[11]谢凤兰,赵改善.利用SVD法重建地震剖面[J].石油物探,1990,29(4):33～40.
[12]高静怀,朱光明,王玉贵.奇异值分解在VSP中的应用[J].西安地质学院学报,1992,14(3):71～78.
[13]詹毅,赵波.自动追踪SVD压制线性干扰方法的改进[J].石油地球物理勘探,2008,43(2):158～160.
[14]赵犁丰,姚玉玲.SVD方法在信号重构中的应用[J].青岛海洋大学学报,1999,29(1):101～106.
[15]裴江云,刘洪,李幼铭.在随机噪声背景下地震反射信号的增强[J].地球物理学进展,2002,17(1):91～95.
[2]Freire S L M,Ulrych T J.Application of singular value decomposition to vertical seismic profiling[J].Geophysics,1988,53(6):778～785.
[3]Maia B,Mirko B.Local singular value decomposition for signal enhancement of seismic data[J].Geophysics,2007,72(2):59～65.
[4]Liu Xuewei.Ground roll suppression using the Karhunen-Loeve transform[J].Geophysics,1999,64(2):564～566.
[5]苑益军,徐林.叠前线性干扰自动追踪SVD压制方法[J].现代地质,2007,21(4):733～737.
[6]陆文凯,牟永光.一种改进的SVD滤波器[J].石油地球物理勘探,1996,31(5):736～741.
[7]刘志鹏,赵伟,陈小宏,等.局部频率域SVD压制随机噪声方法[J].石油地球物理勘探,2012,47(2):202～206.
[8]陈遵德,段天友,朱广生.SVD滤波方法的改进与应用[J].石油地球物理勘探,1994,29(6):783～792.
[9]钟本善,何昌礼,杨忠民.复杂构造地区的SVD去噪技术[J].成都理工学院学报,2000,27(1):93～95.
[10]沈鸿雁,李庆春.频域奇异值分解(SVD)地震波场去噪[J].石油地球物理勘探,2010,25(1):185～189.
[11]谢凤兰,赵改善.利用SVD法重建地震剖面[J].石油物探,1990,29(4):33～40.
[12]高静怀,朱光明,王玉贵.奇异值分解在VSP中的应用[J].西安地质学院学报,1992,14(3):71～78.
[13]詹毅,赵波.自动追踪SVD压制线性干扰方法的改进[J].石油地球物理勘探,2008,43(2):158～160.
[14]赵犁丰,姚玉玲.SVD方法在信号重构中的应用[J].青岛海洋大学学报,1999,29(1):101～106.
[15]裴江云,刘洪,李幼铭.在随机噪声背景下地震反射信号的增强[J].地球物理学进展,2002,17(1):91～95.
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