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Open AccessArticle

Research on the Monitoring of Overlying Aquifer Water Richness in Coal Mining by the Time-Lapse Electrical Method

by Chenyang Zhu, Guowei Zhu, Yufei Gong and Lei Zhang

Energies 2024, 17(8), 1946; https://doi.org/10.3390/en17081946 - 19 Apr 2024

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To study the influence of coal mining on the water richness overlying strata in the mining area using time-lapse electrical monitoring technology, four dataset acquisitions were completed with the same acquisition method, equipment, parameters, and processing flow. According to the characteristics of the data, major problems such as topographic correction, high-precision denoising, spatial and temporal normalization, and resistivity data inversion have been solved. Precise tomographic imaging was achieved through high-precision data processing and difference inversion. The results show that the electrical stratification characteristics of the overlying soil and rock layers are clear, the resistivity from the surface down gradually increases, and the electrical layers are not uniform locally. During mining, the overlying strata are affected by mining, the electrical resistivity of the underlying aquifers increased to varying degrees, and the fluctuation of electrical resistivity increased while the aquifer’s water content decreased. After mining, the overlying aquifer has the phenomenon of ‘reduced resistivity and water recovery’. After a period of time, the overlying soil disturbance and overlying rock failure zone will gradually tend to be stable. Meanwhile, the aquifer structure and water content will also gradually recover. Our results could provide guidance for water resources protection in this region. Full article

(This article belongs to the Collection Energy Efficiency and Environmental Issues)

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20 pages, 7637 KB

Open AccessArticle

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

by Ruiheng Li, Yi Di, Hao Tian and Lu Gan

Electronics 2023, 12(19), 4138; https://doi.org/10.3390/electronics12194138 - 4 Oct 2023

Viewed by 1604

Abstract

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%. Full article

(This article belongs to the Special Issue Mechanism and Modeling of Graph Convolutional Networks)

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