一种优化的复曲波变换压制混叠噪声方法
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摘要
高密度采集可以提高地震资料品质,改善成像精度,但也会增加地震采集成本.为了提高采集效率降低生产成本,混采技术得到了推广应用.但是该采集方式会产生严重的混叠噪声,降低地震数据的信噪比.针对此问题,本文结合中值滤波、动校正(NMO)和复曲波变换阈值去噪的优势,设计了一种优化的复曲波变换压制混源噪声方法.该方法首先采用大步长中值滤波对经过NMO处理的数据进行滤波,再利用基于复曲波域的阈值去噪方法提取剩余信号,计算滤波结果的伪分离记录和原始混叠数据的差值,再将该差值返回到第一步进行迭代,每次迭代中值滤波步长逐步减小,直到达到初始设定的期望信噪比为止.与基于F-K域和curvelet域的迭代阈值方法相比,本文方法可以在压制混叠噪声的同时,更好的保护有效信号,由于本文方法仅需较少的迭代次数,计算效率也可以大大提高.
The high density acquisition way can uplift the subsurface imaging accuracy, whereas the high cost limits the widely application in practice. Blending acquisition way has emerged as a promising way of significantly increasing the efficiency of seismic acquisition. However, there will exist a large challenge of severe interference noise and decrease S/N ratio. Therefore, with recent processing practices, the success of blending acquisition relies heavily on the effectiveness of de-blending to separate signals from simultaneous sources. In the paper, we proposed an optimized blended noise suppression approach combining the advantages of median filter, Normal Moveout(NMO) and Complex Curvelet Transform(CCT). Firstly, the large step median filter is applied to the initial data after NMO correction. Next, we continue to extract the residual energy to get the de-blended result by the CCT-based threshold method. Then, re-iterate the difference data by subtracting the original pseudo de-blended data and the pseudo de-blended data of the de-blended result from each iteration as the above processing flow. Finally, the final de-blended data is derived by adding the remained energy of each iteration until the S/N ratio satisfies the desired one. We demonstrate through a simulated field data the effectiveness of the approach.
The high density acquisition way can uplift the subsurface imaging accuracy, whereas the high cost limits the widely application in practice. Blending acquisition way has emerged as a promising way of significantly increasing the efficiency of seismic acquisition. However, there will exist a large challenge of severe interference noise and decrease S/N ratio. Therefore, with recent processing practices, the success of blending acquisition relies heavily on the effectiveness of de-blending to separate signals from simultaneous sources. In the paper, we proposed an optimized blended noise suppression approach combining the advantages of median filter, Normal Moveout(NMO) and Complex Curvelet Transform(CCT). Firstly, the large step median filter is applied to the initial data after NMO correction. Next, we continue to extract the residual energy to get the de-blended result by the CCT-based threshold method. Then, re-iterate the difference data by subtracting the original pseudo de-blended data and the pseudo de-blended data of the de-blended result from each iteration as the above processing flow. Finally, the final de-blended data is derived by adding the remained energy of each iteration until the S/N ratio satisfies the desired one. We demonstrate through a simulated field data the effectiveness of the approach.
引文
Akerberg P, Hampson G, Rickett J, et al. 2008. Simultaneous source separation by sparse radon transform [C]. 78th Annual International Meeting, SEG, Expanded Abstracts, 2801-2805.
Berkhout A J. 2008. Changing the mindset in seismic data acquisition [J]. The Leading Edge, 27(7): 924-938.
Berkhout A J, Blacquiere G, Verschuur D J. 2009. The concept of double blending: Combining incoherent shooting with incoherent sensing [J]. Geophysics, 74(4): A59-A62.
Chen Y K. 2014. Deblending using a space-varying median filter [J]. Exploration Geophysics, 46(4): 332-341, doi: 10.1071/EG14051.
Chen Y K, Fomel S, Hu J W. 2014. Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization [J]. Geophysics, 79(5): V179-V189.
Dong Lieqian, Wang Changhui, Li Changfen, et al. 2018. Blending noise removal utilizing an adaptive median filter [J]. Progress in Geophysics (in Chinese), 33(4): 1475-1479, doi: 10.6038/pg2018BB0360.
Han L G, Tan C Q, Lv Q T, et al. 2013. Separation of multi-source blended seismic acquisition data by iterative denoising [J]. Chinese J. Geophys (in Chinese), 56(7): 2402-2412, doi: 10.6038/cjg20130726.
Huang M Z, Li P M, Wang Y J. 2012. Two-step suppressing method for neighboring-shot interference by using vibroseis independent simultaneous shooting data [J]. Geophysical prospecting for petroleum (in Chinese), 51(5): 464- 468.
Huo S, Luo Y, Kelamis P G. 2012. Simultaneous sources separation via multidirectional vector-median filtering [J]. Geophysics, 77(4): V123-V131.
Jack L, Taylor B, Howe D, et al. 2008. Independent simultaneous sweeping: a method to increase the productivity of land seismic crews [C]. 78th Annul International Meeting, SEG Expanded Abstracts, 2826-2830.
Liu Y, Liu C, Wang D. 2008. A 1D time-varying median filter for seismic random, spike-like noise elimination [J]. Geophysics, 74(1): V17-V24.
Ramesh N, Anatoly B, Warren S R. 2010. Adaptive subtraction using complex-valued curvelet transforms[J].Geophysics, 75(4): V51-V60.
Zhou Y H, Chen W C. 2018. Separation of simultaneous source data based on sparse inversion [J]. Geophysical Prospecting of Petroleum (in Chinese), 57(1): 33-38.
董烈乾, 汪长辉, 李长芬, 等. 2018. 利用自适应中值滤波方法压制混叠噪声[J]. 地球物理学进展, 33(4): 1475-1479, doi: 10.6038/pg2018BB0360.
韩立国, 谭尘青, 吕庆田, 等. 2013. 基于迭代去噪的多源地震混合采集数据分离[J]. 地球物理学报, 56(7): 2402-2412, doi: 10.6038/cjg20130726.
黄明忠, 李培明, 王彦娟. 2012. 独立同步激发数据两步法邻炮干扰压制技术研究[J]. 石油物探, 51(5): 464- 468.
周艳辉, 陈文超. 2018. 基于稀疏反演的同步震源地震数据分离方法[J]. 石油物探, 57(1): 33-38.
Berkhout A J. 2008. Changing the mindset in seismic data acquisition [J]. The Leading Edge, 27(7): 924-938.
Berkhout A J, Blacquiere G, Verschuur D J. 2009. The concept of double blending: Combining incoherent shooting with incoherent sensing [J]. Geophysics, 74(4): A59-A62.
Chen Y K. 2014. Deblending using a space-varying median filter [J]. Exploration Geophysics, 46(4): 332-341, doi: 10.1071/EG14051.
Chen Y K, Fomel S, Hu J W. 2014. Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization [J]. Geophysics, 79(5): V179-V189.
Dong Lieqian, Wang Changhui, Li Changfen, et al. 2018. Blending noise removal utilizing an adaptive median filter [J]. Progress in Geophysics (in Chinese), 33(4): 1475-1479, doi: 10.6038/pg2018BB0360.
Han L G, Tan C Q, Lv Q T, et al. 2013. Separation of multi-source blended seismic acquisition data by iterative denoising [J]. Chinese J. Geophys (in Chinese), 56(7): 2402-2412, doi: 10.6038/cjg20130726.
Huang M Z, Li P M, Wang Y J. 2012. Two-step suppressing method for neighboring-shot interference by using vibroseis independent simultaneous shooting data [J]. Geophysical prospecting for petroleum (in Chinese), 51(5): 464- 468.
Huo S, Luo Y, Kelamis P G. 2012. Simultaneous sources separation via multidirectional vector-median filtering [J]. Geophysics, 77(4): V123-V131.
Jack L, Taylor B, Howe D, et al. 2008. Independent simultaneous sweeping: a method to increase the productivity of land seismic crews [C]. 78th Annul International Meeting, SEG Expanded Abstracts, 2826-2830.
Liu Y, Liu C, Wang D. 2008. A 1D time-varying median filter for seismic random, spike-like noise elimination [J]. Geophysics, 74(1): V17-V24.
Ramesh N, Anatoly B, Warren S R. 2010. Adaptive subtraction using complex-valued curvelet transforms[J].Geophysics, 75(4): V51-V60.
Zhou Y H, Chen W C. 2018. Separation of simultaneous source data based on sparse inversion [J]. Geophysical Prospecting of Petroleum (in Chinese), 57(1): 33-38.
董烈乾, 汪长辉, 李长芬, 等. 2018. 利用自适应中值滤波方法压制混叠噪声[J]. 地球物理学进展, 33(4): 1475-1479, doi: 10.6038/pg2018BB0360.
韩立国, 谭尘青, 吕庆田, 等. 2013. 基于迭代去噪的多源地震混合采集数据分离[J]. 地球物理学报, 56(7): 2402-2412, doi: 10.6038/cjg20130726.
黄明忠, 李培明, 王彦娟. 2012. 独立同步激发数据两步法邻炮干扰压制技术研究[J]. 石油物探, 51(5): 464- 468.
周艳辉, 陈文超. 2018. 基于稀疏反演的同步震源地震数据分离方法[J]. 石油物探, 57(1): 33-38.