Research on Magnetotelluric Long-Duration Noise Reduction Based on Adaptive Sparse Representation
Rui Zhou, Tonglin Li, Jiangtao Han, Lijia Liu, Zhenyu Guo
Abstract
When magnetotelluric (MT) sounding data are measured in mining areas and urban areas, the useful signals are buried under the surrounding interference sources in the whole period, which completely covers up the useful signals, resulting in jump points and distortion of the response curves. Sparse representation uses atoms in a dictionary to process noisy signals. Based on the arbitrariness of the length of these dictionary atoms, they can effectively suppress noise even if the noise fills the entire observation period. We propose an improved sparse representation based on an adaptive dictionary, which can construct a dictionary according to the characteristics of the data itself (extracting the noise and the useful signals separately) and then automatically filter the noise atoms (which are regarded as noise) in the dictionary to suppress long-duration noise (noise lasts for a long time). For the synthetic data and the measured data, in a comparison with the common signal noise separation methods, the results indicate that the proposed method can more completely extract the profiles of the long-duration noise and greatly increase the signal-to-noise ratio. Moreover, for various noise sources, the proposed method indicates better improved performance, obtaining smoother and more reliable response results.