小波变换压制噪声在单道地震资料处理中的应用
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摘要
海洋区域地质调查中,单道地震勘探以其高分辨率、方便快捷等优点成为揭示浅地层地质构造的重要手段之一.结合前人研究成果并出于保护信号的完整性,对低频部分的噪声采用带通滤波进行滤除,而对高频信号中的随机噪声采用小波阈值去噪方法进行压制处理.在小波阈值去噪过程中,通过比较选用最佳小波基函数sym5对地震剖面进行三层小波分解.结果表明:两种阈值去噪方法在带通滤波的基础上进一步提升了剖面的质量,使得反射层面变得连续可追踪.软阈值去噪滤掉了剖面上的较多信息,去噪效果较为彻底,硬阈值去噪则保留了较多的细节信息.
Single-channel seismic exploration has become one of important means to reveal shallow geologic structure in marine geological investigations with its advantages of easy operation,high-efficient and low-cost. Based on previous research results and for the protection of the integrity of signal,the band-pass filter to filter out the random noise in the low-frequency signal and the wavelet threshold de-noising method to filter the random noise in the high-frequency signal is used. In the experiments,the sym5 wavelet is used to decompose the seismic profile by three layers of wavelet decomposition with soft and hard threshold de-noising. The results show that two threshold de-noising methods further enhance the quality of the section on the basis of the band-pass filter,which makes the reflection event become continuous tracking. The soft threshold de-noising method filters out more information on the profile,and the de-noising method effect is more thorough. And the hard threshold de-noising method retains more details.
Single-channel seismic exploration has become one of important means to reveal shallow geologic structure in marine geological investigations with its advantages of easy operation,high-efficient and low-cost. Based on previous research results and for the protection of the integrity of signal,the band-pass filter to filter out the random noise in the low-frequency signal and the wavelet threshold de-noising method to filter the random noise in the high-frequency signal is used. In the experiments,the sym5 wavelet is used to decompose the seismic profile by three layers of wavelet decomposition with soft and hard threshold de-noising. The results show that two threshold de-noising methods further enhance the quality of the section on the basis of the band-pass filter,which makes the reflection event become continuous tracking. The soft threshold de-noising method filters out more information on the profile,and the de-noising method effect is more thorough. And the hard threshold de-noising method retains more details.
引文
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[22]王蓓,张根耀,李智,等.基于新阈值函数的小波阈值去噪算法[J].计算机应用,2014,34(5):1 499-1 502.
[23]毕迎春,王相海.小波基和图像分解层数对不同类型图像EZW算法的性能的影响[J].计算机科学,2006,33(6):232-235.
[24]杨飒.医学图像融合中最佳小波分解层数的选择[J].计算机工程与设计,2008,29(20):5 265-5 268.
[25]龚建周,刘彦随,夏北成,等.HIS和小波变换结合多源遥感影像融合质量对小波分解层数的响应[J].中国图像图形学报,2010,15(8):1 269-1 277.
[26]李军峰,肖都,孔广胜,等.单道海上反射地震在海上物探过程中的应用[J].物探与化探,2004,28(4):365-368.
[27]刘建勋.提高海上单道反射地震记录信噪比和分辨率的方法技术[J].物探化探计算技术,2007,29(s1):116-120.
[28]李庆忠.走向精确勘探的道路[M].北京:石油工业出版社,1994:106-112.
[29]何汉漪.海上高分辨率地震技术及其应用[M].北京:地质出版社,2001:10-17.
[2]Fink C R,Spence G D.Hydrate distribution off Vancouver Island from multifrequency single-channel seismic reflection data[J].Journal of Geophysical Research Atmospheres,1999,104(B2):2 909-2 922.
[3]Mcgee T M.A single-channel seismic reflection method for quantifying lateral variations in BSR reflectivity[J].Marine Geology,2000,164(1):29-35.
[4]李丽青,陈泓君,彭学超,等.海洋区域地质调查中的高分辨率单道地震资料关键处理技术[J].物探与化探,2011,35(1):86-92.
[5]褚宏宪,杨源,张晓波,等.高分辨率单道地震调查数据采集技术方法[J].海洋地质前沿,2012,28(12):70-74.
[6]温珍河,张训华,郝天珧,等.我国海洋地学编图现状、计划与主要进展[J].地球物理学报,2014(12):3 907-3 919.
[7]郑江龙,许江,李海东,等.海上单道地震勘探中船舶等背景噪声的影响分析及压制[J].应用海洋学学报,2015,34(1):17-23.
[8]卢胜周,李振,马秀敏,等.单道地震勘探应用中的干扰因素分析及压制[J].四川地质学报,2016,36(2):310-315.
[9]Gnanadurai D,Sadasivam V.An efficient adaptive thresholding technique for wavelet based image denoising[J].International Journal of Information and Communication Engineering,2006,2(2):114-119.
[10]Mohideen S K,Perumal S A,Sathik M M.Image de-noising using discrete wavelet transform[J].International Journal of Computer Science and Network Security,2008,8(1):213-216.
[11]李雪英,张晶,孔祥琦,等.基于离散小波变换的地震资料自适应高频噪声压制[J].物探与化探,2013,37(1):165-170.
[12]姚建红.基于小波变换的地震勘探信号处理技术研究[D].大庆:大庆石油学院,2009:17-29.
[13]储鹏鹏.基于小波变换的图像去噪方法研究[D].西安:西安电子科技大学,2009:7-17.
[14]桑燕芳,王栋.水文序列小波分析中小波函数选择方法[J].水利学报,2008,39(12):295-301.
[15]Muhammad S,Asaad Y S,Bruce W M.Comparative study of different wavelet based neural network models for rain-runoff modeling[J].Journal of Hydrology,2014,55(4):47-58.
[16]张华,陈小宏,杨海燕.地震信号去噪的最优小波基选取方法[J].石油地球物理勘探,2011,46(1):70-75.
[17]钟建军,宋健,由长喜,等.基于信噪比评价的阈值优选小波去噪法[J].清华大学学报:自然科学版,2014,54(2):259-263.
[18]Donoho D L,Johnstone I M.Ideal spatial adaptation via wavelet shrinkage[J].Biometrika,1994,81:425-455.
[19]Kozowski B.Time series denoising with wavelet transform[J].Industrial Control Computer,2005,3(1):91-95.
[20]张德丰.MATLAB小波分析[M].2.北京:机械工业出版社,2011:296-301.
[21]陈晓曦,王延杰,刘恋.小波阈值去噪法的深入研究[J].激光与红外,2012,42(1):105-110.
[22]王蓓,张根耀,李智,等.基于新阈值函数的小波阈值去噪算法[J].计算机应用,2014,34(5):1 499-1 502.
[23]毕迎春,王相海.小波基和图像分解层数对不同类型图像EZW算法的性能的影响[J].计算机科学,2006,33(6):232-235.
[24]杨飒.医学图像融合中最佳小波分解层数的选择[J].计算机工程与设计,2008,29(20):5 265-5 268.
[25]龚建周,刘彦随,夏北成,等.HIS和小波变换结合多源遥感影像融合质量对小波分解层数的响应[J].中国图像图形学报,2010,15(8):1 269-1 277.
[26]李军峰,肖都,孔广胜,等.单道海上反射地震在海上物探过程中的应用[J].物探与化探,2004,28(4):365-368.
[27]刘建勋.提高海上单道反射地震记录信噪比和分辨率的方法技术[J].物探化探计算技术,2007,29(s1):116-120.
[28]李庆忠.走向精确勘探的道路[M].北京:石油工业出版社,1994:106-112.
[29]何汉漪.海上高分辨率地震技术及其应用[M].北京:地质出版社,2001:10-17.