| 高阶统计量在大地电磁测深数据处理中的应用研究 |
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| 引用本文： | 王书明,王家映.高阶统计量在大地电磁测深数据处理中的应用研究[J].地球物理学报,2004,47(5):928-934. |
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| 作者姓名： | 王书明 王家映 |
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| 作者单位： | 中国地质大学地球物理与空间信息学院,武汉,430074;中国地质大学地球物理与空间信息学院,武汉,430074 |
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| 基金项目： | 国家自然科学基金资助项目 ( 4 0 2 740 3 9) |
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| 摘 要： | 在大地电磁（MT）测深法中，大地电磁响应函数经常出现个别频点分散、误差棒较大、形态异常等现象，在作反演解释时，许多地质特征难以有效提取出来，这些问题严重阻碍了MT的实际应用效能和发展.本文利用高阶统计量方法检验了MT信号的高斯性. 结果表明，MT信号是非高斯的. 根据任何高斯过程，其高阶统计量（高阶累积量，高阶谱）均为零的性质，通过信号的高阶谱恢复功率谱，再由功率谱估算MT响应函数，能有效抑制高斯有色噪声的影响，提高MT资料的处理质量. 从应用效果看，这种方法在抑制高斯有色噪声、提取信号中有用信息方面优于传统功率谱方法.
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| 关 键 词： | 信号处理 高阶统计量 有色噪声 大地电磁 |
| 文章编号： | 0001-5733(2004)05-0928-07 |
| 收稿时间： | 2003-6-27 |
| 修稿时间： | 2004-2-29 |
APPLICATION OF HIGHER-ORDER STATISTICS IN MAGNETOTELLURIC DATA PROCESSING |
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| WANG Shu-Ming WANG Jia-Ying Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan ,China.APPLICATION OF HIGHER-ORDER STATISTICS IN MAGNETOTELLURIC DATA PROCESSING[J].Chinese Journal of Geophysics,2004,47(5):928-934. |
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| Authors: | WANG Shu-Ming WANG Jia-Ying Institute of Geophysics and Geomatics China University of Geosciences Wuhan China |
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| Institution: | Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China |
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| Abstract: | Magnetotelluric (MT) responses some times have scattering at individual frequencies with big error bars or shape distortion, which lower the effectiveness of MT curves in describing underground electrical structures. The main reason for this is that natural MT signal is usually weak, and the traditional data processing method has weakness. This paper uses the higher-order statistics method to test the Gaussianity of MT signals. The results show that the signals are non-Gaussian. On the basis that the higher-order statistics vanish for any Gaussian process, this paper uses higher-order spectra to reconstruct power spectra, then estimates MT responses using reconstructed power spectra, to effectively suppress Gaussian colored noise. Synthetic tests show that the method presented in this paper is superior to the power spectral method in suppressing Gaussian colored noise, and can extract more useful information. |
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| Keywords: | Signal processing Higher-order statistics Colored noise Magnetotelluric |
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