De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation-Reference-Cited by-同舟云学术

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De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation

Published:2020-01 Issue: Volume:172 Page:103919
ISSN:0926-9851
Container-title:Journal of Applied Geophysics
language:en
Short-container-title:Journal of Applied Geophysics

Author:

Li Guang,Liu Xiaoqiong,Tang Jingtian,Li Jin,Ren Zhengyong,Chen Chaojian

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Science and Technology Project of Jiangxi Provincial Education Department

National Key R&D Program of China

Publisher

Elsevier BV

Subject

Geophysics

Reference42 articles.

1. A combinatorial filtering method for magnetotelluric data series with strong interference;Cai;Arab. J. Geosci.,2016

2. Fast dictionary learning for noise attenuation of multidimensional seismic data;Chen;Geophys. J. Int.,2017

3. Subspace Pursuit for Compressive Sensing Signal Reconstruction;Dai;IEEE Trans. Inf. Theory,2009

4. Robust estimation of geomagnetic transfer functions;Egbert;Geophys. J. Int.,1986

5. Polarisation analysis of magnetotelluric time-series using a wavelet-based scheme: a method for detection and characterisation of cultural noise sources;Escalas;Phys. Earth Planet. Interiors,2013

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