【摘要】：噪聲污染是煤巖動(dòng)力災(zāi)害電磁監(jiān)測(cè)應(yīng)用中需要解決的重要問題,去噪效果的好壞直接影響災(zāi)害預(yù)測(cè)的準(zhǔn)確性。經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)是目前電磁信號(hào)去噪中應(yīng)用最多的一種方法,但當(dāng)信號(hào)與噪聲時(shí)頻特征相近時(shí),該算法存在嚴(yán)重的內(nèi)蘊(yùn)模態(tài)函數(shù)(Intrinsic Mode Function,IMF)混疊現(xiàn)象(即部分模態(tài)函數(shù)仍為信號(hào)與噪聲的組合)。針對(duì)該問題,提出一種基于經(jīng)驗(yàn)?zāi)B(tài)分解和頻域約束獨(dú)立成分分析的去噪方法,首先利用EMD將電磁信號(hào)分解為多個(gè)IMF分量,通過計(jì)算各分量與原信號(hào)間的互相關(guān)系數(shù)判斷存在模態(tài)混疊現(xiàn)象過渡IMF,再以過渡IMF后續(xù)分量的頻域?yàn)榧s束條件,對(duì)過渡IMF進(jìn)行獨(dú)立成分分析,去除過渡分量中的噪聲;最后將去噪后的過渡分量與其后續(xù)分量進(jìn)行重構(gòu),得到去噪后的信號(hào)。分別以含噪Ricker子波和現(xiàn)場電磁信號(hào)為例,利用信噪比定量驗(yàn)證了上述方法對(duì)處理現(xiàn)場電磁信號(hào)模態(tài)混疊問題的有效性,同時(shí)頻域約束條件下的獨(dú)立成分分析去噪收斂快、效率高,適合海量實(shí)時(shí)監(jiān)測(cè)信號(hào)快速去噪使用。
[Abstract]:Noise pollution is an important problem to be solved in the application of electromagnetic monitoring of coal and rock dynamic disasters. The effect of noise removal directly affects the accuracy of disaster prediction. Empirical mode decomposition (Empirical Mode Decomposition,EMD) is one of the most widely used methods in electromagnetic signal denoising at present. However, when the signal and noise time and frequency characteristics are close, the algorithm has serious intrinsic mode function (Intrinsic Mode Function,. IMF) aliasing (i.e. partial mode function is still a combination of signal and noise). To solve this problem, a denoising method based on empirical mode decomposition (EMD) and frequency domain constrained independent component analysis (ICA) is proposed. Firstly, the electromagnetic signal is decomposed into multiple IMF components by EMD. By calculating the correlation number between each component and the original signal, the transition IMF, is judged to exist mode aliasing phenomenon. Then, the transition IMF is analyzed by independent component analysis (ICA) to remove the noise in the transition component by taking the frequency domain of the subsequent component of the transition IMF as the constraint condition. Finally, the de-noised transition component and its subsequent components are reconstructed to obtain the de-noised signal. Taking the noisy Ricker wavelet and the field electromagnetic signal as examples, the effectiveness of the above method in dealing with the field electromagnetic signal mode aliasing problem is quantitatively verified by using SNR, and the independent component analysis (ICA) denoising convergence is fast under the constraint of frequency domain. High efficiency and suitable for fast denoising of massive real-time monitoring signal.
【作者單位】： 福州大學(xué)環(huán)境與資源學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(51604083)
【分類號(hào)】：TD326

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