Magnetotelluric Noise Suppression Based on Impulsive Atoms and NPSO-OMP Algorithm
- PDF / 1,716,522 Bytes
- 23 Pages / 547.087 x 737.008 pts Page_size
- 43 Downloads / 180 Views
Pure and Applied Geophysics
Magnetotelluric Noise Suppression Based on Impulsive Atoms and NPSO-OMP Algorithm JIN LI,1 XIAOQIONG LIU,1,2 GUANG LI,2,3 Abstract—The magnetotelluric (MT) method is a mainstream geophysical exploration method widely used in deep mineral resource exploration and other fields. However, the MT signal is extremely vulnerable to noise since it employs natural electromagnetic fields as a source. In order to improve the signal-to-noise ratio, a novel MT noise suppression method based on impulsive atoms and a niche particle swarm optimization-orthogonal matching pursuit (NPSO-OMP) algorithm is proposed. First, an overcomplete dictionary composed of impulsive atoms is well-developed to match different types of cultural noise. Then, the sparse decomposition process of OMP is optimized with NPSO. Finally, the original MT data are de-noised using the well-developed dictionary and the NPSO-OMP algorithm. By processing the synthetic and measured MT data, and comparing the experimental results with those by traditional methods, we found that the proposed method can effectively suppress the high-intensity impulsive noise in the MT signal under the premise of retaining useful information. The continuity of apparent resistivity and the phase curve are significantly improved, and the results are verified by the remote reference method and robust statistic estimation. When the observation data are subjected to persistent, strongly correlated noise pollution, our method can obtain better results than the remote reference method and robust statistic estimation. Thus, when it is difficult to obtain high-quality MT response curves through the remote reference method and robust statistic estimation, our method is a promising alternative. Keywords: Magnetotellurics (MT), impulsive atoms, niche particle swarm optimization (NPSO), orthogonal matching pursuit (OMP), noise suppression.
1 College of Information Science and Engineering, Hunan Normal University, Changsha 410081, Hunan, China. 2 Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province, East China University of Technology, Nanchang 330013, Jiangxi, China. E-mail: [email protected] 3 Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of Education, Central South University, Changsha 410083, Hunan, China.
and JINGTIAN TANG3 1. Introduction
The magnetotelluric (MT) method is a popular and indispensable method in the field of deep mineral resource exploration and many other fields (Fu¨llekrug and Constable 2000; Simpson and Bahr 2005; Zhao et al. 2017; Tang et al. 2018; Arisbaya et al. 2019; Devi et al. 2019). However, natural MT signals are sometimes weak, non-stationary and therefore susceptible to man-made noise (Neukirch and Garcia 2014; Cai 2016; Guo et al. 2019; Li et al. 2020a). Methods for processing noisy MT data mainly include remote refer
Data Loading...
