Denoising algorithm of ground-airborne time-domain electromagnetic method based on Variational Bayesian-based adaptive Kalman filter (VBAKF)

Wu, Qiong; Ma, Yunfeng; Li, Dongsheng; Wang, Yuan; Ji, Yanju

doi:10.1016/j.jappgeo.2022.104674

Cited by 15 publications

(1 citation statement)

References 25 publications

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“…The process of inferring the electrical media structure from acquired time-domain electromagnetic induction signals is termed TEM inversion [15,16]. Strata media inversion based on single stations is commonly utilized for extensive geological surveys, often involving substantial data processing [13,17,18]. Traditional optimization methods like Newton's gradient descent are computationally inefficient for massive TEM survey data [15,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

TEM Strata Inversion Imaging with IP Effect Based on Enhanced GCN by Extracting Long-Dependency Features

Li,

Di,

Tian

et al. 2023

Electronics

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Utilizing neural network models to inverse time-domain electromagnetic signals enables rapid acquisition of electrical structures, a non-intrusive method widely applied in geological and environmental surveys. However, traditional multi-layer perceptron (MLP) feature extraction is limited, struggling with cases involving complex electrical media with induced polarization effects, thereby limiting the inversion model’s predictive capacity. A graph-topology-based neural network model for strata electrical structure imaging with long-dependency feature extraction was proposed. We employ graph convolutional networks (GCN) for capturing non-Euclidean features like resistivity-thickness coupling and Long Short-Term Memory (LSTM) to capture long-dependency features. The LSTM compensates for GCN’s constraints in capturing distant node relationships. Using case studies with 5-strata and 9-strata resistivity models containing induced polarization effects, compared to traditional MLP networks, the proposed model utilizing time-domain features and graph-topology-based electrical structure extraction significantly improves performance. The mean absolute error in inversion misfit is reduced from 10–20% to around 2–3%.

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