Ore-controlling structures of the Xiangshan volcanic Basin, SE China: Revealed from three-dimensional inversion of Magnetotelluric data

J Deng, H Yu, H Chen, Z Du, H Yang, H Li, S Xie… - Ore Geology …, 2020 - Elsevier
We report a high-resolution three-dimensional (3D) Magnetotelluric (MT) study to define the
mineralizing system of the Xiangshan (XS) volcanic Basin, which hosts the largest volcanic …

Dictionary learning and shift-invariant sparse coding denoising for controlled-source electromagnetic data combined with complementary ensemble empirical mode …

G Li, Z He, J Tang, J Deng, X Liu, H Zhu - Geophysics, 2021 - library.seg.org
Controlled-source electromagnetic (CSEM) data recorded in industrialized areas are
inevitably contaminated by strong cultural noise. Traditional noise attenuation methods are …

Denoising of magnetotelluric data using K‐SVD dictionary training

J Li, Y Peng, J Tang, Y Li - Geophysical Prospecting, 2021 - earthdoc.org
Magnetotelluric is one of the mainstream exploration geophysical methods, which plays a
vital role in studying deep geological structures and finding deep hidden blind ore bodies …

Deep learning optimized dictionary learning and its application in eliminating strong magnetotelluric noise

G Li, X Gu, Z Ren, Q Wu, X Liu, L Zhang, D Xiao… - Minerals, 2022 - mdpi.com
The noise suppression method based on dictionary learning has shown great potential in
magnetotelluric (MT) data processing. However, the constraints used in the existing …

Identification and suppression of magnetotelluric noise via a deep residual network

L Zhang, Z Ren, X Xiao, J Tang, G Li - Minerals, 2022 - mdpi.com
The magnetotelluric (MT) method is widely applied in petroleum, mining, and deep Earth
structure exploration but suffers from cultural noise. This noise will distort apparent resistivity …

[HTML][HTML] Synthesizing magnetotelluric time series based on forward modeling

P Wang, X Chen, Y Zhang - Frontiers in Earth Science, 2023 - frontiersin.org
The validity of magnetotelluric time-series processing methods has been lacking reasonable
testing criteria. Since the time series synthesized by existing techniques are not fully derived …

Low-Frequency Magnetotelluric Data Denoising Using Improved Denoising Convolutional Neural Network and Gated Recurrent Unit

G Li, X Gu, C Chen, C Zhou, D Xiao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The magnetotelluric (MT) signals are susceptible to anthropogenic noise and the existing
denoising methods have significant shortcomings in low-frequency situations. To address …

Magnetotelluric noise attenuation using a deep residual shrinkage network

G Zuo, Z Ren, X Xiao, J Tang, L Zhang, G Li - Minerals, 2022 - mdpi.com
Magnetotelluric (MT) surveying is an essential geophysical method for mapping subsurface
electrical conductivity structures. The MT signal is susceptible to cultural noise, and the …

Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

X Zhang, J Li, D Li, Y Li, B Liu, Y Hu - Earth, Planets and Space, 2021 - Springer
Magnetotelluric (MT) data processing can increase the reliability of measured data.
Traditional MT data denoising methods are usually applied to entire MT time-series, which …

Identification and Suppression of Multi-component Noise in Audio Magnetotelluric based on Convolutional Block Attention Module

L Zhang, G Li, H Chen, J Tang, G Yang… - … on Geoscience and …, 2024 - ieeexplore.ieee.org
Audio magnetotelluric (AMT) is commonly used in mineral resource exploration. However,
the weak energy of AMT signals makes them susceptible to being overwhelmed by noise …