论文中文题名: | 小波变换及其在地震资料去噪中的应用 |
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学号: | 03034 |
保密级别: | 公开 |
学科代码: | 070104 |
学科名称: | 应用数学 |
学生类型: | 硕士 |
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论文外文题名: | Wavelet Transform and Application in Seismic Data Denoising |
论文中文关键词: | 小波变换 ; 小波去噪 ; 地震资料 ; 二次小波分解全局阈值 |
论文外文关键词: | |
论文中文摘要: |
小波变换是八十年代后期发展起来的应用数学分支,具有多分辨分析的特点,而且在时频两域都具有表征信号局部特性的能力。由于小波分析的时频分析特征,很快就成功地应用于地球物理信号处理与解释中。本文将小波分析中几种小波基的去噪性能进行了比较分析,确定了处理信号时小波母函数的选取问题。小波全局阈值方法是小波分析中处理信号的常用方法,本文就该方法进行了讨论,指出该方法在处理高频含噪信号时所表现的不足;进而提出二次小波分解全局阈值方法,该方法在对含有高频有用信息的信号去噪时,效果优于全局阈值方法,并且指出该方法的适用范围。
由于地震资料采集的客观条件等因素,在实际地震资料中不可避免地包含了随机噪声。本文对地震资料中随机噪声的去除进行了分析。先对理论合成地震资料用全局阈值方法进行处理,对于水平或倾角较小的同相轴该方法处理效果较好,但对倾角较大的同相轴全局阈值方法处理效果不理想。将合成资料进行小波分解,其各尺度高频部分都含有倾角较大的同相轴信息,由此利用二次小波分解全局阈值方法对同一地震合成资料处理,去噪后的结果中将倾角较大的同相轴信息较好地保留下来,但同时又保留了少量的噪声信号。对此本文又将该地震资料利用小波全局阈值方法进行第二次处理,结果去除了大部分剩余的噪声,同时有效信号的能量也相对完整地保留下来。最后,本文将该方法应用于实际地震资料去噪处理中,随机噪声基本得到去除,反射同相轴连续性较好。
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论文外文摘要: |
Wavelet transform,emerging as a novel technique, has been shown to be very useful in many field. Wavelet are functions that are both localized in the time and the frequency domains. Because of this property, both time and frequency characteristics of the signal are captured in Wavelet transform. Therefore, Wavelet transform was applied to noise on removal seismic record.
In the paper, several typical wavelet functions were evaluated. The denoising study on testing signal provided the optimum denoising strategy. Then signal was analyzed with the global threshold denoising method in this paper. Disadvantage is shown if the signal contained high-frequency information. Because of this, the method of second-decomposition on wavelet global threshold was suggested.
In practice, seismic data usually contained information of random noise for the impersonal condition. Firstly, theoretical model was studied with wavelet global threshold denoising method. For the same phase axle of steep obliquity, this denoising method is not ideal enough. So with the method of second-decomposition on wavelet global threshold, information of the same phase axle of steep obliquity was held preferably, compared with wavelet global threshold denoising method. But information of random noise was too at the same time. Farther, the data was processed second denoising with global threshold denoising method, and the effect was preferable. At last, this method was used to practical seismic data in the paper.
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中图分类号: | P631.4 |
开放日期: | 2007-04-06 |