Characterization of Excitation Effects and Data Interpretation of Combined Time-Domain Multiwaveform Transmission Currents

Wang, Shipeng; Yu, Yibing; Zhao, Xuejiao; Wang, Yuan; Gao, Quanming; Ji, Yanju

doi:10.1109/jsen.2023.3287317

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Using coils or long straight wires as signal transmitters generates time-domain-induced electromagnetic signals underground, enabling inference of the basic structure of subsurface electrical media [1][2][3][4]. This non-intrusive geophysical method, known as transient electromagnetic (TEM) detection, is extensively employed for subsurface imaging and exploration [5][6][7], particularly for low-resistivity target areas [8].…”

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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