A New F-PCASVD based Multivariate Denoising Algorithm for FID Transversal Data

Abstract

The free induction decay (FID) transversal data determines the measurement accuracy of time-dependent geomagnetic fields, whereas the conservation of clean components and removal of noise cannot be easily achieved for this kind of data. Even though numerous techniques have been proven to be effective in improving the signal-to-noise ratio (SNR) by filtering out frequency bands, how to efficiently reduce noise is still a crucial issue. To end this, a new multivariate algorithm based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD), namely F-PCASVD, was presented. This novel algorithm aims to reduce noise as well as cancel the interference of FID transversal data. Specifically, the F-PCASVD algorithm is able to obtain the dominant principal components of the FID and that of the noise floor by PCA, in which an optimal number of subspaces could be retained via a cumulative percent of variance criterion. Furthermore, the PCA was combined with an SVD filter whose singular values corresponding to the interferences were identified, and then the noise was suppressed by nulling the corresponding singular values to zero, which was able to achieve an optimum trade-off between the preservation of pure FID data and the denoising efficiency. Our proposed F- PCASVD algorithm was compared with the widely used filter methods via extensive experiments on synthetic and real FID transversal data. The results demonstrated that this method shows a significant improvement in noise suppression.