Seismic Data De-noising Based on Second Wavelet Transform

2008 International Conference on Advanced Computer Theory and Engineering Pub Date : 2008-12-20 DOI:10.1109/ICACTE.2008.118

Fu Yan, Zhang Chunqin

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引用次数: 3

Abstract

A main task of geophysical exploration is to remove random noises in seismic data processing to improve the signal-to-noise ratio. Recently wavelet theory is applied widely to remove random noises in seismic data processing. A commonly used de-noising method is represented by Donoho. On the basis of Donoho's wavelet threshold de-noising processing method, the paper presents a de-noising method for seismic data based on second wavelet transform. The multi-scale wavelet transform is carried out for seismic data in the method, then second multi-scale wavelet transform is carried out again for wavelet coefficients in scale 1 mainly controlled by noises, zero is set for wavelet coefficient in scale 1 after second wavelet transform and reconstruction of wavelet coefficients in other scales is carried out, finally, the wavelet threshold de-noising processing is carried out for seismic section after above-mentioned processing. The results of theoretical model and practical data processing show that the method presented by the paper can effectively improve processing quality of seismic sections and S/N ratio of seismic data.

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基于二次小波变换的地震数据去噪

地球物理勘探的主要任务是去除地震资料处理中的随机噪声，提高地震资料的信噪比。近年来，小波理论被广泛应用于地震资料处理中的随机噪声去除。一种常用的降噪方法是多诺霍。在Donoho小波阈值去噪处理方法的基础上，提出了一种基于二次小波变换的地震数据去噪方法。该方法对地震资料进行多尺度小波变换，然后对主要受噪声控制的1尺度小波系数再次进行二次多尺度小波变换，对1尺度小波系数进行二次小波变换后置零，对其他尺度小波系数进行重构，最后对上述处理后的地震剖面进行小波阈值去噪处理。理论模型和实际数据处理结果表明，该方法能有效提高地震剖面处理质量和地震资料信噪比。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2008 International Conference on Advanced Computer Theory and Engineering

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