Intelligent inversion of magnetotelluric data based on improved DenseNet
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 infe-rior timeliness and reliance on initial model settings.In this study,deep learning was applied to the one-dimensional inversion of mag-netotelluric data.First,an improved DenseNet model was constructed and trained to invert geological models of various resistivity-varia-ble 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 tech-nique 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 re-sults.
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