小波尺度域和空间域倾角扫描高阶相关去噪技术
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摘要
针对常规小波相关去噪仅在尺度域或空间域进行二阶相关去噪的局限,本文提出了一种小波尺度域和空间域高阶相关去噪方法。首先对地震记录剖面进行5个尺度的静态小波分解,得到5个分量记录剖面,对这5个剖面的每道在尺度域对相邻尺度的小波系数进行高阶相关;然后对经尺度域相关的5个剖面在空间域对同一分量记录剖面相邻道的小波系数再进行高阶相关,并用此系数对相应道修正,完成所有分量剖面的空间域相关后的重构,最终得到去噪后的地震记录剖面。数值试验和实际资料处理结果表明,该方法对随机噪声的去噪效果明显优于仅在尺度域或空间域进行二阶相关的小波去噪的效果。
In view of the limitation that denoise of ordinary wavelet correlation is to use second-order correlation denoise only in scale domain or space domain, the paper presented a method for denoise of high-order correlation in wavelet scale or space domains. First, the five-scale static wavelet decomposition is carried out for seismic section, getting five component sections, and for each trace of five sections the high-order correlation is carried out for wavelet coefficient of adjacent scales in scale domain; then, for five sections after correlation in scale domain the high-order correlation is carried out again for wavelet coefficient of adjacent traces in same component section in space domain, and using the coefficient to modify the corresponding trace, which finished reconstruction after all component sections being correlated in space domain, and post-denoise seismic section is finally obtained. The numeric test and processing results of practical data showed that the method has evident denoise effect for random noise and is superior to wavelet denoise method only using second-order correlation in scale or space domain.
In view of the limitation that denoise of ordinary wavelet correlation is to use second-order correlation denoise only in scale domain or space domain, the paper presented a method for denoise of high-order correlation in wavelet scale or space domains. First, the five-scale static wavelet decomposition is carried out for seismic section, getting five component sections, and for each trace of five sections the high-order correlation is carried out for wavelet coefficient of adjacent scales in scale domain; then, for five sections after correlation in scale domain the high-order correlation is carried out again for wavelet coefficient of adjacent traces in same component section in space domain, and using the coefficient to modify the corresponding trace, which finished reconstruction after all component sections being correlated in space domain, and post-denoise seismic section is finally obtained. The numeric test and processing results of practical data showed that the method has evident denoise effect for random noise and is superior to wavelet denoise method only using second-order correlation in scale or space domain.
引文
[1]李庆忠.走向精确勘探的道路.北京:石油工业出版社,1994,6~24
[2]Donoho D L.De-noising by soft thresholding.IEEE Transactions on IT,1995,41(3):613~627
[3]Xu Yet al.Wavelet transform domain filters:a spa-tially selective noise filtration technique.IEEE Trans Image Processing,1994,3(6):747~758
[4]Mallat S,Hwang WL.Singularity detection and pro-cessing with wavelets.IEEE Trans Inform Theory,1992,38(2):617~643
[5]潘泉,张磊,孟晋丽等.小波滤波方法及应用.北京:清华大学出版社,2005,63~72
[6]白英奎,申铉国,孟宪江.空域相关滤噪法的改进及用于红外光谱信号处理.激光与红外,2004,34(1):37~39
[7]曹汉强,陈汝钧,李旭涛等.杂乱背景中隐藏信号的三阶相关检测技术.光电工程,2005,32(9):5~11
[8]张贤达.现代信号处理.北京:清华大学出版社,2002,267
[9]张三宗,徐义贤.地震记录小波域高阶相关叠加技术.地球物理学报,2006,49(2):554~560
[10]Pan Quan,Zhang Lei.Two de-noising methods by wavelets transform.IEEE Trans Signal Processing,1999;47(12):3401~3406
[11]赵瑞珍,屈汉章,宋国乡.基于小波系数区域相关性的阈值滤波算法.西安电子科技大学学报,2001,28(3):324~327
[12]Zhang Lei,Paul Bao.De-noising by spatial correlation thresholding.IEEE Transactions On Circuits and System for Video Technology,2003,13(6):535~538
[13]强琳,刘贵忠.利用小波变换多尺度传播特性实现地震信号去噪.西安交通大学学报,1998,32(1):29~32
[14]付燕.基于小波变换的地震信号分时分频相关去噪.煤田地质与勘探,2002,30(6):52~54
[15]杨述斌,彭复员,谢志远等.基于相关和小波分析的激光水下目标检测研究.计算机仿真,2003,20(5):17~19
[16]徐长发,李国宽.实用小波方法.武汉:华中科技大学出版社,2001,99~100
[17]樊计昌,刘明军,海燕.地震数据处理中小波和小波包最大分解层的确定.科技导报,2008,26(10):40~42
[18]郭显久,王伟.基于尺度乘积与小波收缩相结合的去噪方法.控制与决策,2005,20(6):698~701
[2]Donoho D L.De-noising by soft thresholding.IEEE Transactions on IT,1995,41(3):613~627
[3]Xu Yet al.Wavelet transform domain filters:a spa-tially selective noise filtration technique.IEEE Trans Image Processing,1994,3(6):747~758
[4]Mallat S,Hwang WL.Singularity detection and pro-cessing with wavelets.IEEE Trans Inform Theory,1992,38(2):617~643
[5]潘泉,张磊,孟晋丽等.小波滤波方法及应用.北京:清华大学出版社,2005,63~72
[6]白英奎,申铉国,孟宪江.空域相关滤噪法的改进及用于红外光谱信号处理.激光与红外,2004,34(1):37~39
[7]曹汉强,陈汝钧,李旭涛等.杂乱背景中隐藏信号的三阶相关检测技术.光电工程,2005,32(9):5~11
[8]张贤达.现代信号处理.北京:清华大学出版社,2002,267
[9]张三宗,徐义贤.地震记录小波域高阶相关叠加技术.地球物理学报,2006,49(2):554~560
[10]Pan Quan,Zhang Lei.Two de-noising methods by wavelets transform.IEEE Trans Signal Processing,1999;47(12):3401~3406
[11]赵瑞珍,屈汉章,宋国乡.基于小波系数区域相关性的阈值滤波算法.西安电子科技大学学报,2001,28(3):324~327
[12]Zhang Lei,Paul Bao.De-noising by spatial correlation thresholding.IEEE Transactions On Circuits and System for Video Technology,2003,13(6):535~538
[13]强琳,刘贵忠.利用小波变换多尺度传播特性实现地震信号去噪.西安交通大学学报,1998,32(1):29~32
[14]付燕.基于小波变换的地震信号分时分频相关去噪.煤田地质与勘探,2002,30(6):52~54
[15]杨述斌,彭复员,谢志远等.基于相关和小波分析的激光水下目标检测研究.计算机仿真,2003,20(5):17~19
[16]徐长发,李国宽.实用小波方法.武汉:华中科技大学出版社,2001,99~100
[17]樊计昌,刘明军,海燕.地震数据处理中小波和小波包最大分解层的确定.科技导报,2008,26(10):40~42
[18]郭显久,王伟.基于尺度乘积与小波收缩相结合的去噪方法.控制与决策,2005,20(6):698~701
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