Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself.

An Optimised Signal Reconstruction Algorithm for Compressed Sensing

Signal recovery is a key issue in compressed sensing field. A new greedy reconstruction algorithm termed Optimised Stagewise Orthogonal Matching Pursuit (OSOMP) is proposed, which is an improved version for Stagewise Orthogonal Matching Pursuit (StOMP). In preselection step, OSOMP chooses several coordinates with a calculated threshold to accelerate the convergence of algorithm. In following pruning step, a small proportion of selected coordinates are discarded according to the amplitude of estimated signal, thus most false discovered coordinates can be swept away. Experimental results show that in OSOMP, the scale of estimated support can be controlled very well, and the successful recovery rate is also much higher than that in StOMP.