On the Nature of Negative Values of Induced Polarization in the Transient Electromagnetic Process when Working with an Axial Electrical Installation on the Sea Shelf

Ageev, V. V.

doi:10.1134/s1069351323020027

Cited by 1 publication

(1 citation statement)

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“…When subsurface electrical media exhibit induced polarization (IP) effects [28,29], charges rearrange, impacting the trend of electromagnetic induction signals within specific time windows [30][31][32][33][34]. This further exacerbates the nonlinearity of TEM inversion, posing a greater challenge to feature extraction in neural network inversion models.…”

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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