航空电磁数据主成分滤波重构的噪声去除方法

主成分分析方法利用低阶主成分重构航空电磁数据,解决了航空电磁探测中噪声与数据在频谱重叠情况下的噪声压制问题,但是参与重构的低阶主成分仍包含高频空间噪声,影响数据成像精度.本文提出的主成分滤波重构去噪方法,根据自适应窗宽平滑算法,设计了主成分低通滤波器组,对参与重构的低阶主成分进行测线滤波,再将滤波后的低阶主成分重构为电磁信号,不仅可以去除低阶主成分中的高频空间噪声,而且去除了高阶主成分包含的不相关噪声.仿真数据的去噪结果表明,主成分滤波重构获得较高的信噪比,较常规测线滤波与主成分重构分别提高了10.96dB和2.52dB;电导率深度成像结果证明了主成分滤波重构方法能够提高地下深部异常体的识别能力.最后通过实测数据的成像结果进一步验证了本文研究的主成分滤波重构去噪方法的有效性.

Noise removal based on reconstruction of filtered principal components

Airborne electromagnetic data has complex noise due to the flight environment, changes of system parameters and device parameters. The traditional time-frequency approach is difficult to remove noise. PCA(principal component analysis)can remove the noise which overlaps with the signal frequency spectrum. However the low-order components still contain high frequency spatial noise. To solve this problem, this work proposes the reconstruction of filtered principal components which can not only remove the high-frequency spatial noise of low-order components, but also remove uncorrelated noise of high-order components. This approach uses filtered principal components to reconstruct electromagnetic data. Firstly, it computes eigenvectors and eigenvalues matrix of the correlation matrix and the principal component profiles which are uncorrelated. The low-order principal components associated with the big eigenvalues reflect the correlated electromagnetic signals, while high-order principal components associated with the small eigenvalues are corresponding to the uncorrelated noise. It operates by first filtering the principal component profiles with the widest filter. Then according to local variation of each smoothed principal component profile, a group of low-pass filters is designed. Local variation of each smoothed principal component profile is converted linearly into adaptive smoothing filter window width of the measuring point. So each principal component profile is filtered by the corresponding filter. Finally, filtered low-order principal components are used to reconstruct electromagnetic signal. This paper designed a pseudo-2D earth model with a deep target. The noise added to it drowned the target of later channels and the CDI(conductivity depth imaging)cannot reflect the deep target. After PCA processing, principal component profiles of noise-contaminated data have high frequency spatial noise compared with those of the forward data. The peak to peak of noise amplitude of the first principal component is about 0.005 nT/s. With an adaptive window wide filter, noise of the principal component profile is significantly reduced and consistent with the forward data mostly. The data is processed by three de-noising methods. The later channels profiles by the traditional profile filter have no obvious target and CDI cannot reflect the deep target either. The later channels profiles by principal component reconstruction still contain high frequency noise, impacting identification of the deep target. But data by filtered principal component reconstruction not only eliminates high frequency noise, but also ensures the magnitude of the target. It shows the more accurate position and shape. SNR is improved by 10.96 dB and 2.52 dB relative to the other two methods. This work deniosed one survey line measured in Henan Province. The CDI results of other two methods exhibit low-resistance target diffusion phenomenon to different degrees and cannot show exact location of the target for this survey line. But the CDI by filtered principal component reconstruction indicates that the algorithm in this paper can suppress high frequency noise effectively, consistent with real local condition.ATEM data is converted into the principal component domain. A group of low-pass filters is able to change the filter bandwidth adaptively according to local variation of each principal component profile. It can not only filter out high frequency spatial noise, but also maintain the target amplitude. At the same time, it also improves the SNR of latest channel data and enhances the ability to identify the deep target after filtered principal component reconstruction. Filtered principal component reconstruction is an important complement to noise processing of time and frequency domain, providing a new line of thought for decomposition and synthesis of the transform domain and de-noising processing of geophysical data.