Deep Learning Based Geological Interpretation of AMT Data
AMT(Audio Magnetotelluric)is widely used for obtaining geological condition related to sandstone-type uranium deposits,such as the range of buried sand body and the top boundary of basement rock.However,these geological condition are hard to interpret via measured sections without geological deduction,which relies heavily on the experience and cognition of the interpreter.On the other hand,with the development of 3D technology,artificial geological interpretation shows low efficiency and reliability.In this paper,a deep learning model constructed using U-net was used for the geological interpretation of AMT data in the Naren-Yihegaole area.To train the model,a training dataset was built based on the simulated data from random simulated models.In the prediction stage,sand bodies and basement rock were delineated from the inversion resistivity images.The comparison between two interpretations,one by deep learning method,showed high consistency with the artificial one,but with better time-saving.Therefore,the deep learning based technology is more effective than the traditional way.
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