Curvelet域蒙特卡罗估计的随机噪声衰减
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摘要
针对低信噪比地震资料进行噪声压制时,传统滤波方法容易损伤有效波。研究认为,curvelet多尺度多方向的分析能力可以有效分离随机噪声,提出基于蒙特卡罗估计的自适应非线性阈值函数法衰减噪声能量,实现在压制噪声的同时保持有效反射信息。模型算例及大巴山地区某地震资料的处理实例表明,该方法能够有效地压制随机干扰,同相轴连续性与剖面信噪比较传统小波方法显著提高,一定程度上改善了常规滤波处理方法在压制噪声的同时对有效波的影响。
For the random noise suppressing in seismic records with low signal to noise ratio,traditional methods will harm the signal components. The paper thinks curvelet transform can separate the random noise using multi-scale and multi-direction. The authors apply Monte Carlo estimator to compute the noise level and design a non-linear thresholding function to remove the random noise coefficients,so the useful signal will be recovered. Applications on both synthetic data and actual seismic data from Dabashan area show that the new method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data. The quality and consecutive of seismic event are better as well as the quality of section is improved obviously,and it overcomes the drawback that the conventional filtering approach may affect the effective wave when suppressing noise.
For the random noise suppressing in seismic records with low signal to noise ratio,traditional methods will harm the signal components. The paper thinks curvelet transform can separate the random noise using multi-scale and multi-direction. The authors apply Monte Carlo estimator to compute the noise level and design a non-linear thresholding function to remove the random noise coefficients,so the useful signal will be recovered. Applications on both synthetic data and actual seismic data from Dabashan area show that the new method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data. The quality and consecutive of seismic event are better as well as the quality of section is improved obviously,and it overcomes the drawback that the conventional filtering approach may affect the effective wave when suppressing noise.
引文
[1]李仲远.南方碳酸盐岩地区地震采集技术方法探讨[J].天然气工业,2009,29(6):33–36.
[2]吕金博,孔剑冰,张绍华,等.复杂地表条件下低信噪比地震资料的处理技术[J].天然气工业,2007,27(增刊A):111–112.
[3]安勇,杨长春.一种改进的频率—波数域倾角扫描去噪方法[J].石油地球物理勘探,2008,43(2):210–212.
[4]杨海涛,朱仕军,杨爱国,等.S变换时变滤波在去噪处理中的应用研究[J].西南石油大学学报:自然科学版,2009,31(6):56–58.
[5]屠志慧,李楠.三维地震资料高保真处理流程研究[J].西南石油大学学报:自然科学版,2008,30(3):65–67.
[6]高静怀,毛剑,满蔚仕,等.叠前地震资料噪声衰减的小波域方法研究[J].地球物理学报,2006,49(4):1155–1163.
[7]Cand`es E J,Donoho D L.Curvelet,manuscript[EB/OL].http://www-stat.standford.edu/donoho/Reports/1999/cur-velets.pdf
[8]Cand`es E J,Donoho D L.Continuous curvelet transform[J].Appl Comput Harmon Anal,2005(19):198–222.
[9]张恒磊,张云翠,宋双,等.基于Curvelet域的叠前地震资料去噪方法[J].石油地球物理勘探,2008,43(5):508–513.
[10]张恒磊,刘天佑,张云翠.基于高阶相关的Curvelet域和空间域的倾角扫描噪声压制方法[J].石油地球物理勘探,2010,45(2):208–214.
[11]Neelamani R,Baumstein A I,Dominique G G,et al.Co-herent and random noise attenuation using the curvelettransform[J].The Leading Edge,2008,27:240–248.
[12]Herrmann F J,Hennenfent G.Non-parametric seis-mic data recovery with curvelet frames[J].Geophysics,2008(173):233–248.
[13]Herrmann F J,Wang D,Hennenfent G,et al.Curvelet-based seismic data processing:A multiscale and nonlinearapproach[J].Geophysics,2008,73:A1–A5.
[14]Douma H,Maarten V H.Leading-order seismic imag-ing using curvelets[J].Geophysics,2007,72(1):S231–S235.
[15]刘军.科学计算中的蒙特卡罗策略[M].唐年胜,周勇,徐亮,译.北京:高等教育出版社,2009.
[2]吕金博,孔剑冰,张绍华,等.复杂地表条件下低信噪比地震资料的处理技术[J].天然气工业,2007,27(增刊A):111–112.
[3]安勇,杨长春.一种改进的频率—波数域倾角扫描去噪方法[J].石油地球物理勘探,2008,43(2):210–212.
[4]杨海涛,朱仕军,杨爱国,等.S变换时变滤波在去噪处理中的应用研究[J].西南石油大学学报:自然科学版,2009,31(6):56–58.
[5]屠志慧,李楠.三维地震资料高保真处理流程研究[J].西南石油大学学报:自然科学版,2008,30(3):65–67.
[6]高静怀,毛剑,满蔚仕,等.叠前地震资料噪声衰减的小波域方法研究[J].地球物理学报,2006,49(4):1155–1163.
[7]Cand`es E J,Donoho D L.Curvelet,manuscript[EB/OL].http://www-stat.standford.edu/donoho/Reports/1999/cur-velets.pdf
[8]Cand`es E J,Donoho D L.Continuous curvelet transform[J].Appl Comput Harmon Anal,2005(19):198–222.
[9]张恒磊,张云翠,宋双,等.基于Curvelet域的叠前地震资料去噪方法[J].石油地球物理勘探,2008,43(5):508–513.
[10]张恒磊,刘天佑,张云翠.基于高阶相关的Curvelet域和空间域的倾角扫描噪声压制方法[J].石油地球物理勘探,2010,45(2):208–214.
[11]Neelamani R,Baumstein A I,Dominique G G,et al.Co-herent and random noise attenuation using the curvelettransform[J].The Leading Edge,2008,27:240–248.
[12]Herrmann F J,Hennenfent G.Non-parametric seis-mic data recovery with curvelet frames[J].Geophysics,2008(173):233–248.
[13]Herrmann F J,Wang D,Hennenfent G,et al.Curvelet-based seismic data processing:A multiscale and nonlinearapproach[J].Geophysics,2008,73:A1–A5.
[14]Douma H,Maarten V H.Leading-order seismic imag-ing using curvelets[J].Geophysics,2007,72(1):S231–S235.
[15]刘军.科学计算中的蒙特卡罗策略[M].唐年胜,周勇,徐亮,译.北京:高等教育出版社,2009.
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