(吉林大學(xué) 儀器科學(xué)與電氣工程學(xué)院，長春 130026)
摘 要: 時(shí)間域航空電磁數(shù)據(jù)經(jīng)預(yù)處理后，存在殘余噪聲，嚴(yán)重影響晚期道數(shù)據(jù)質(zhì)量。提出一種基于主成分分析的去噪方法，通過對航空電磁剖面數(shù)據(jù)的自相關(guān)矩陣進(jìn)行奇異值分解，得到特征值及對應(yīng)的特征向量，將電磁數(shù)據(jù)通過旋轉(zhuǎn)矩陣（特征向量矩陣的轉(zhuǎn)置）進(jìn)行線性映射，得到其主成分。大特征值對應(yīng)的低階主成分反映相關(guān)性較強(qiáng)的電磁信號，而小特征值對應(yīng)的高階主成分反映不相關(guān)噪聲。采用低階主成分與特征向量重構(gòu)電磁數(shù)據(jù)能夠去除不相關(guān)噪聲。采用主成分分析法分別進(jìn)行了仿真數(shù)據(jù)與實(shí)測數(shù)據(jù)的主成分特征分析與噪聲去除。仿真數(shù)據(jù)去噪后，信噪比提高了13 dB；而野外飛行實(shí)測數(shù)據(jù)的噪聲水平也由±25 nT/s 降低到±8 nT/s。
關(guān)鍵字: 時(shí)間域航空電磁；主成分分析；特征值；特征向量；去噪
（School of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China）
Abstract:There is still residual noise which affects the quality of later channel data after preprocessing for time domain airborne electromagnetic data. An approach was proposed to remove the residual noise based on principal component analysis. The principal components were computed through the rotation matrix which is the transpose of eigenvectors matrix. The low-order principle components associated with the big eigenvalues reflect the correlated electromagnetic signals, while high-order principle components associated with the small eigenvalues are corresponding to the uncorrelated the noise. Therefore, the electromagnetic data are reconstructed by suitable number of the low-order components to remove uncorrelated noise. The experimental results of the simulation data show that the SNR is improved of 13 dB. The peak to peak value of the latest two channels for the field survey profile data is reduced from ±25 nT/s to ±8 nT/s after noise removal.
Key words: time domain airborne electromagnetic method; principal component analysis; eigenvalue; eigenvector; noise removal
