A Magnetotelluric Data Denoising Method Based on Lightweight Ensemble Learning-Reference-Cited by-同舟云学术

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A Magnetotelluric Data Denoising Method Based on Lightweight Ensemble Learning

Published:2024 Issue: Volume:62 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ji Mingjie¹,Chen Huang²^ORCID,Zhang Chao²,Yu Nian³^ORCID,Kong Wenxin²^ORCID

Affiliation:

1. School of Electrical Engineering, Chongqing University, Chongqing, China

2. School of Resources and Safety Engineering and the State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing, China

3. School of Electrical Engineering and the State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10530937.pdf?arnumber=10530937

Reference58 articles.

1. On determining electrical characteristics of the deep layers of the Earths crust;Tikhonov;SSSR,1986

2. BASIC THEORY OF THE MAGNETO‐TELLURIC METHOD OF GEOPHYSICAL PROSPECTING

3. Construction and destruction of the North China Craton with implications for metallogeny: Magnetotelluric evidence from the Hengshan–Wutai–Fuping region within Trans-North China Orogen

4. New development of the Electromagnetic (EM) methods for deep exploration;Di;Chin. J. Geophys.,2019

5. Implications for the lithospheric structure of Cambay rift zone, western India: Inferences from a magnetotelluric study

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