Intelligent inversion of magnetotelluric data based on improved DenseNet
YAO Yu1(), ZHANG Zhi-Hou2()
1. China Railway Design Cooperation, TianJin 300143, China 2. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Magnetotelluric (MT) sounding is a vital exploration method in tunnel engineering. Inversion methods can assist geologists in interpreting geological data by converting MT data into geoelectric parameters. However, conventional inversion methods exhibit inferior timeliness and reliance on initial model settings. In this study, deep learning was applied to the one-dimensional inversion of magnetotelluric data. First, an improved DenseNet model was constructed and trained to invert geological models of various resistivity-variable strata, yielding a fast computational speed and high accuracy. Then, the robustness of the improved DenseNet model was tested, suggesting that its network structure can achieve satisfactory inversion results for noisy data. Finally, this artificial intelligence technique was applied to the MT data inversion of the Hongjiaqian tunnel in the Huangshan area, obtaining geophysical exploration results that match the geological research results. Additionally, relevant construction recommendations were given based on the inversion results.
姚禹, 张志厚. 基于改进DenseNet的大地电磁智能反演[J]. 物探与化探, 2024, 48(3): 759-767.
YAO Yu, ZHANG Zhi-Hou. Intelligent inversion of magnetotelluric data based on improved DenseNet. Geophysical and Geochemical Exploration, 2024, 48(3): 759-767.
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