基于Curvelet变换的地震噪声衰减
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摘要
在研究地震勘探中,随机噪声是一种频带较宽的干扰波,常规的去噪方法效果不理想。为对方位提高识别能力,首先根据具有多尺度与多方向性的Curvelet变换的基本原理及其实现方法。采用块阈值法应用于地震数据随机噪声衰减中,并进行了仿真计算和实际资料的处理。结果证明利用Curvelet变换块阈值法能比较彻底地去除噪声,去噪后的图像边缘保持良好,滤除噪声同时还保留了有效部分,去噪效果良好,具有小波变换无可比拟的优势,且易于实现,在地震资料处理中具有一定的可行性和应用价值。
In the seismic exploration,random noise is a wide band disturbing wave,which is not ideal when denoised by conventional means. The basic principle and realization of curvelet transform,which has multi-scale and multi-direction characteristics,were introduced. And it was applied in the random noise decaying of seismic data by adopting lump threshold,and in the simulation and real data processing. The result proved that curvelet transform can relatively completely remove the noise while the edge of picture kept well and the detail part also kept at the same time. The result of noise decaying was good and better than wavelet and easy to realize,so that curvelet transform has the feasibility and prospect in the seismic data processing.
In the seismic exploration,random noise is a wide band disturbing wave,which is not ideal when denoised by conventional means. The basic principle and realization of curvelet transform,which has multi-scale and multi-direction characteristics,were introduced. And it was applied in the random noise decaying of seismic data by adopting lump threshold,and in the simulation and real data processing. The result proved that curvelet transform can relatively completely remove the noise while the edge of picture kept well and the detail part also kept at the same time. The result of noise decaying was good and better than wavelet and easy to realize,so that curvelet transform has the feasibility and prospect in the seismic data processing.
引文
[1]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,2003.
[2]TJ Brown.An adaptive strategy for wavelet based image enhance-ment[C].Proceeding of Irish machine vision and image proceed-ing conference,Belfast:Northern Ireland,2000.
[3]Emmanuel Candès,Laurent Demanet.Fast Discrete Curvelet Transforms[J].Applied and Computational Mathematics,2005.
[4]赵心.基于Curvelet变换的图像去噪方法研究与应用[D].山东科技大学,2007.
[5]蔡炳煌.基于曲波分析的图像处理与应用[D].汕头大学,2007.27-29.
[6]何劲,李宏伟,张帆.基于Curvelet变换与小波包变换联合的图像去噪算法[J].通信技术,2008,41(1):140-142.
[7]肖小奎,黎绍发.加强边缘保护的Curvelet图像去噪方法[J].通信学报,2004,25(2):9-15.
[8]程远航,薛定宇,韩晓微.基于小波和曲波结合的图像增强算法[J].计算机技术与应用进展,2007.
[9]吕文彪,尹成,张白林,田继东,李大卫.利用独立分量分析法去除地震噪声[J].石油地球物理勘探,2004,42(2):132-136.
[10]张旗,梁德群,樊鑫,李文举.基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报,2004,23(4):281-285.
[2]TJ Brown.An adaptive strategy for wavelet based image enhance-ment[C].Proceeding of Irish machine vision and image proceed-ing conference,Belfast:Northern Ireland,2000.
[3]Emmanuel Candès,Laurent Demanet.Fast Discrete Curvelet Transforms[J].Applied and Computational Mathematics,2005.
[4]赵心.基于Curvelet变换的图像去噪方法研究与应用[D].山东科技大学,2007.
[5]蔡炳煌.基于曲波分析的图像处理与应用[D].汕头大学,2007.27-29.
[6]何劲,李宏伟,张帆.基于Curvelet变换与小波包变换联合的图像去噪算法[J].通信技术,2008,41(1):140-142.
[7]肖小奎,黎绍发.加强边缘保护的Curvelet图像去噪方法[J].通信学报,2004,25(2):9-15.
[8]程远航,薛定宇,韩晓微.基于小波和曲波结合的图像增强算法[J].计算机技术与应用进展,2007.
[9]吕文彪,尹成,张白林,田继东,李大卫.利用独立分量分析法去除地震噪声[J].石油地球物理勘探,2004,42(2):132-136.
[10]张旗,梁德群,樊鑫,李文举.基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报,2004,23(4):281-285.
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