【摘要】：根據(jù)大地電磁噪聲的特點(diǎn)和獨(dú)立分量分析(ICA)中M-FastICA算法的優(yōu)良性能,結(jié)合小波分析和盲源分離的相關(guān)理論,提出了一種改進(jìn)的獨(dú)立分量分析去噪方法。首先對(duì)觀測(cè)信號(hào)進(jìn)行多尺度小波分解,使信號(hào)從單道變成多道,以滿足獨(dú)立分量分析對(duì)觀測(cè)信號(hào)的數(shù)目需求;然后采用M-FastICA算法對(duì)小波分解提取的多層高頻分量進(jìn)行獨(dú)立分量分析以提取有效獨(dú)立分量和特定獨(dú)立分量;引入動(dòng)態(tài)自適應(yīng)因子來限制特定獨(dú)立分量的權(quán)重以減小觀測(cè)信號(hào)信噪比對(duì)去噪效果的影響;最后由小波低頻分量和M-FastICA算法提取的兩種獨(dú)立分量共同構(gòu)成恢復(fù)信號(hào)。模擬信號(hào)仿真實(shí)驗(yàn)表明,該方法的去噪性能優(yōu)于傳統(tǒng)小波閾值去噪方法。將該方法應(yīng)用于實(shí)際大地電磁觀測(cè)資料的去噪處理,無論是視電阻率曲線還是相位曲線,都比去噪前更加光滑和穩(wěn)定,說明改進(jìn)的獨(dú)立分量分析算法能有效地去除大地電磁噪聲。
[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.
【作者單位】： 長(zhǎng)江大學(xué)油氣資源與勘探技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室;非常規(guī)油氣湖北省協(xié)同創(chuàng)新中心;長(zhǎng)江大學(xué)信息與數(shù)學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金(41274082,U1562109) 長(zhǎng)江大學(xué)長(zhǎng)江青年基金(2015cqn76);長(zhǎng)江大學(xué)重磁電勘探研究中心創(chuàng)新基金(7011201803xm)聯(lián)合資助~~
【分類號(hào)】：P631.325

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