Suppression of random noise in deep seismic reflection data using adaptive threshold-based Shearlet transform
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摘要: 深反射地震探测技术是研究地球深部地质结构最为有效的手段之一。受大地滤波作用的影响,地震波能量成指数衰减,造成深层有效反射能量弱,受背景噪声干扰严重,难以实现深部地质结构的准确成像。本文通过研究深反射地震数据在Shearlet域中各个尺度角度上有效信号和随机噪声的分布差异,发现在不同的尺度角度上地震信号受随机噪声的影响程度不同。进一步将深反射地震数据尺度角度域中的信噪比、Shearlet系数二范数及随机噪声残差作为阈值的估计参数,实现随尺度角度自适应的随机噪声压制方法,最大限度地去除随机噪声的影响。通过理论模型数据和实际深反射地震数据测试,验证了Shearlet域自适应阈值随机噪声压制方法可以有效地去除随机噪声干扰,提升地震剖面的整体信噪比,实现深层微弱反射信号的精准成像。
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关键词:
- 深反射地震 /
- Shearlet变换 /
- 自适应阈值 /
- 信噪比 /
- 地震弱信号
Abstract: Deep seismic reflection is one of the most effective means of studying the deep geological structure of the Earth.However,the energy of seismic waves exponentially decreases due to the filtering by the Earth,resulting in weak energy of effective deep seismic reflection signals.In this case,deep seismic reflection data are liable to be seriously disturbed by background noise,and thus it is difficult to obtain accurate images of deep geological structures.According to the study on the differences in the distribution of effective signals and random noise of deep seismic reflection data on different scales in the Shearlet domain,seismic signals on different scales are affected by random noise to different extents.Furthermore,with the signal-to-noise ratio,the L2 norm of Shearlet coefficients,and the residual errors of random noise in deep seismic reflection data as the parameters for threshold estimation,this study developed a random noise suppression method that is adaptive to different scales to minimize the effects of random noise.Theoretical model data and actual tests of deep seismic reflection data verified that this method can effectively eliminate the disturbance of random noise,improve the overall signal-to-noise ratio of seismic sections,and realize the accurate imaging of weak deep seismic reflection signals. -
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[1] 王海燕, 高锐, 卢占武, 等. 国内外深反射地震研究的进展及应用研究[J]. 中国西部油气地质, 2005, 1(1):101-107.
[2] Wang H Y, Gao R, Lu Z W, et al. Progress and application of deep seismic reflection research domestic and over seas[J]. West China Petroleum Geosciences, 2005, 1(1):101-107.
[3] 徐泰然, 卢占武, 王海燕, 等. 深反射地震剖面数据处理的主要技术方法[J]. 地球物理学进展, 2017, 32(2):326-338.
[4] Xu T R, Lu Z W, Wang H Y, et al. Main technology of deep seismic reflection data processing[J]. Progress in Geophysics, 2017, 32(2):326-338.
[5] 李胜强, 刘振东, 严加永, 等. 高分辨深反射地震探测采集处理关键技术综述[J]. 地球物理学进展, 2020, 35(4):190-199.
[6] Li S Q, Liu Z D, Yan J Y, et al. Review on the key techniques for high resolution deep reflection seismic acquisition and processing[J]. Progress in Geophysics, 2020, 35(4):190-199.
[7] 朱小三, 高锐, 李秋生, 等. 深反射地震数据的噪音衰减方法综述[J]. 地球物理学进展, 2013, 28(6):2878-2900.
[8] Zhu X S, Gao R, Li Q S, et al. Review of the noises attenuation of deep reflection seismic data[J]. Progress in Geophysics, 2013, 28(6):2878-2900.
[9] 谭晓迪, 黄大年, 李丽丽, 等. 小波结合幂次变换方法在边界识别中的应用[J]. 吉林大学学报:地球科学版, 2018, 48(2):82-94.
[10] Tan X D, Huang D N, Li L L, et al. Application of wavelet transform combined with power transform method in edge detection[J]. Journal of Jilin University:Earth Science Edition, 2018, 48(2):82-94.
[11] 刘霞, 黄阳, 黄敬, 等. 基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J]. 吉林大学学报:地球科学版, 2016, 46(1):262-269.
[12] Liu X, Huang Y, Huang J, et al. Wavelet entropy threshold seismic signal denoising based on empirical mode decomposition (EMD)[J]. Journal of Jilin University:Earth Science Edition, 2016, 46(1): 262-269.
[13] 程浩, 袁月, 王恩德, 等. 基于小波变换的自适应阈值微震信号去噪研究[J]. 东北大学学报:自然科学版, 2018, 38(9):1332-1336.
[14] Cheng H, Yuan Y, Wang E D, et al. Study of Hierarchical adaptive threshold micro-seismic signal denoising based on wavelet transform[J]. Journal of Northeastern University:Natural Science, 2018, 38(9):1332-1336.
[15] 罗红梅, 宋维琪, 邢漪冉, 等. 基于改进经验模态分解的地震弱信号增强处理方法[J]. 地球物理学进展, 2019, 34(1):173-179.
[16] Luo H M, Song W Q, Xing Y R, et al. Seismic weak signal enhancement processing method based on improved empirical mode decomposition[J]. Progress in Geophysics, 2019, 34(1):173-179.
[17] 陈亮, 陈丽芳, 刘保相. 一种神经网络改进小波的地震数据随机噪声去除方法[J]. 地震工程学报, 2019, 41(2):476-481.
[18] Chen L, Chen L F, Liu B X. A method for random noise elimination from seismic data based on the Neural Network-improved wavelet transform[J]. China Earthquake Engineering Journal, 2019, 41(2):476-481.
[19] Guo K, Labate D, Lim W Q, et al. Wavelets with composite dilations[J]. Electronic Research Society, 2004, 10(9):78-87.
[20] Guo K, Labate D, Lim W Q, et al. Wavelets with composite dilations and their MRA properties[J]. Applied and Computational Harmonic Analysis, 2006, 20(2):202-236.
[21] 王通, 王德利, 冯飞, 等. 三维稀疏反演多次波预测及曲波域匹配相减技术[J]. 吉林大学学报:地球科学版, 2017, 47(6):1865-1874.
[22] Wang T, Wang D L, Feng F, et al. Multiple prediction with 3D sparse inversion and Curvelet match[J]. Journal of Jilin University:Earth Science Edition, 2017, 47(6):1865-1874.
[23] 刘磊, 刘振, 张军华. 曲波阈值法地震弱信号识别及去噪方法研究[J]. 地球物理学进展, 2011, 29(4):1415-1422.
[24] Liu L, Liu Z, Zhang J H. Denoising and detecting seismic weak signal based on curvelet thresholding method[J]. Progress in Geophysics, 2011, 29(4):1415-1422.
[25] 金丹, 程建远, 王保利, 等. 基于Curvelet变换的地震资料弱信号识别及去噪方法[J]. 煤炭学报, 2016, 41(2):332-337.
[26] Jin D, Cheng J Y, Wang B L, et al. Seismic weak signal identification and noise elimination based on curvelet domain[J]. Jounrnal of China Coal Society, 2016, 41(2):332-337.
[27] 何柯, 周丽萍, 于宝利, 等. 基于补偿阈值的曲波变换地面微地震弱信号检测方法[J]. 物探与化探, 2016, 40(1):55-60.
[28] He K, Zhou L P, Yu B L, et al. The ground microseismic weak signal detection method based on compensation threshold of curvelet transform[J]. Geophysical and Geochemical Exploration, 2016, 40(1):55-60.
[29] 张华, 陈小宏, 李红星, 等. 曲波变换三维地震数据去噪技术[J]. 石油地球物理勘探, 2017, 52(2):226-232.
[30] Zhang H, Chen X H, Li H X, et al. A denoising technique for 3D seismic data by Curvelet transform[J]. Oil Geophysical Prospecting, 2017, 52(2):226-232.
[31] 孙苗苗, 李振春, 曲英铭, 等. 基于曲波域稀疏约束的OVT域地震数据去噪方法研究[J]. 石油物探, 2019, 58(2):58-68.
[32] Sun M M, Li Z C, Qu Y M, et al. A seismic denoising method based on Curvelet transform with sparse constraint in OVT domain[J]. Geophysical Prospecting for Petroleum, 2019, 58(2):58-68.
[33] 刘成明, 王德利, 王通, 等. 基于Shearlet变换的地震随机噪声压制[J]. 石油学报, 2014, 35(4):692-699.
[34] Liu C M, Wang D L, Wang T, et al. Random seismic noise attenuation based on the Shearlet transform[J]. Acta Petrolei Sinica, 2014, 35(4):692-699.
[35] 程浩, 王德利, 王恩德, 等. 尺度自适应三维Shearlet变换地震随机噪声压制[J]. 石油地球物理勘探, 2019, 54(5):36-44.
[36] Cheng H, Wang D L, Wang E D, et al. Scaling adaptive 3D shearlet transform seismic random noise suppression[J]. Oil Geophysical Prospecting, 2019, 54(5):36-44.
[37] 童思友, 高航, 刘锐, 等. 基于Shearlet变换的自适应地震资料随机噪声压制[J]. 石油地球物理勘探, 2019, 54(4):744-750.
[38] Tong S Y, Gao H, Liu R, et al. Shearlet transform based adaptive random noise suppression of seismic data[J]. Oil Geophysical Prospecting, 2019, 54(4):744-750.
[39] 薛林, 程浩, 巩恩普, 等. Shearlet域自适应阈值地震数据随机噪声压制[J]. 石油地球物理勘探, 2020, 55(2):282-291.
[40] Xue L, Cheng H, Gong E P, et al. Shearlet domain adaptive threshold seismic data random noise suppression[J]. Oil Geophysical Prospecting, 2020, 55(2):282-291.
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