一种改进的独立分量分析算法在大地电磁去噪中的应用
发布时间：2019-04-22 10:21
【摘要】：根据大地电磁噪声的特点和独立分量分析(ICA)中M-FastICA算法的优良性能,结合小波分析和盲源分离的相关理论,提出了一种改进的独立分量分析去噪方法。首先对观测信号进行多尺度小波分解,使信号从单道变成多道,以满足独立分量分析对观测信号的数目需求;然后采用M-FastICA算法对小波分解提取的多层高频分量进行独立分量分析以提取有效独立分量和特定独立分量;引入动态自适应因子来限制特定独立分量的权重以减小观测信号信噪比对去噪效果的影响;最后由小波低频分量和M-FastICA算法提取的两种独立分量共同构成恢复信号。模拟信号仿真实验表明,该方法的去噪性能优于传统小波阈值去噪方法。将该方法应用于实际大地电磁观测资料的去噪处理,无论是视电阻率曲线还是相位曲线,都比去噪前更加光滑和稳定,说明改进的独立分量分析算法能有效地去除大地电磁噪声。
[Abstract]:According to the characteristics of magnetotelluric noise and the excellent performance of M-FastICA algorithm in independent component analysis (ICA), an improved independent component analysis (ICA) de-noising method is proposed based on wavelet analysis and blind source separation theory. Firstly, multi-scale wavelet decomposition is used to transform the signal from a single channel to a multi-channel, so as to meet the needs of independent component analysis for the number of observed signals. Then the M-FastICA algorithm is used to analyze the multi-layer high-frequency components extracted by wavelet decomposition to extract the effective independent components and the specific independent components. The dynamic adaptive factor is introduced to limit the weight of specific independent components to reduce the influence of signal-to-noise ratio on the de-noising effect. Finally, two independent components extracted by wavelet low-frequency component and M-FastICA algorithm are used to construct the restored signal. Simulation results show that the de-noising performance of the proposed method is better than that of the traditional wavelet threshold de-noising method. The method is applied to the de-noising processing of the magnetotelluric observation data. Both the apparent resistivity curve and the phase curve are smoother and more stable than those before the de-noising. It is shown that the improved independent component analysis algorithm can effectively remove the magnetotelluric noise.
【作者单位】： 长江大学油气资源与勘探技术教育部重点实验室;非常规油气湖北省协同创新中心;长江大学信息与数学学院;
【基金】：国家自然科学基金(41274082,U1562109) 长江大学长江青年基金(2015cqn76);长江大学重磁电勘探研究中心创新基金(7011201803xm)联合资助~~
【分类号】：P631.325
[Abstract]:According to the characteristics of magnetotelluric noise and the excellent performance of M-FastICA algorithm in independent component analysis (ICA), an improved independent component analysis (ICA) de-noising method is proposed based on wavelet analysis and blind source separation theory. Firstly, multi-scale wavelet decomposition is used to transform the signal from a single channel to a multi-channel, so as to meet the needs of independent component analysis for the number of observed signals. Then the M-FastICA algorithm is used to analyze the multi-layer high-frequency components extracted by wavelet decomposition to extract the effective independent components and the specific independent components. The dynamic adaptive factor is introduced to limit the weight of specific independent components to reduce the influence of signal-to-noise ratio on the de-noising effect. Finally, two independent components extracted by wavelet low-frequency component and M-FastICA algorithm are used to construct the restored signal. Simulation results show that the de-noising performance of the proposed method is better than that of the traditional wavelet threshold de-noising method. The method is applied to the de-noising processing of the magnetotelluric observation data. Both the apparent resistivity curve and the phase curve are smoother and more stable than those before the de-noising. It is shown that the improved independent component analysis algorithm can effectively remove the magnetotelluric noise.
【作者单位】： 长江大学油气资源与勘探技术教育部重点实验室;非常规油气湖北省协同创新中心;长江大学信息与数学学院;
【基金】：国家自然科学基金(41274082,U1562109) 长江大学长江青年基金(2015cqn76);长江大学重磁电勘探研究中心创新基金(7011201803xm)联合资助~~
【分类号】：P631.325
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