Noise removal for airborne electromagnetic data based on principal component analysis
Noise removal for airborne electromagnetic data based on principal component analysis
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发表时间：
2013
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通讯作者：
Zhu Kai-guan
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作者：
Zhu Kai-guan
针对时间域航空电磁数据在预处理后仍存在残余噪声影响后续通道数据质量的问题，提出了一种基于主成分分析的残余噪声去除方法，通过特征向量矩阵的转置得到旋转矩阵计算主成分，与大特征值相关的低阶主成分反映了相关电磁信号，而与小特征值相关联的高阶主分量对应于不相关的噪声。因此，电磁数据由适当数量的低-仿真数据的实验结果表明，信噪比提高了13 dB，野外测量剖面数据的最后两个通道的峰峰值比噪声去除后从±25 nT/s降低到±8 nT/s。
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.