Conventional interpretation of airborne electromagnetic data has been conducted by solving the inverse problem. However, with recent advances in machine learning (ML) techniques, a 1D deep neural network inversion that predicts a 1D resistivity model using multifrequency vertical magnetic fields and sensor height information at one location has been applied. Nevertheless, since the final interpretation of this 1D approach relies on connecting 1D resistivity models, 1D ML interpretation has low accuracy for the estimation of an isolated anomaly, as in conventional 1D inversion. Thus, we have developed a 2D interpretation technique that can overcome the limitations of 1D interpretation and consider spatial continuity by using a recurrent neural network (RNN). We generate various 2D resistivity models, calculate the ratio of primary and induced secondary magnetic fields of the vertical direction in ppm scale using a vertical magnetic dipole source, and then train the RNN using the resistivity models and the corresponding electromagnetic (EM) responses. To verify the validity of 2D RNN inversion, we apply the trained RNN to synthetic and field data. Through application of the field data, we demonstrate that the design of the training data set is crucial to improve prediction performance in a 2D RNN inversion. In addition, we investigate changes in the RNN inversion results of field data dependent on the data preprocessing. We demonstrate that using two types of data, logarithmic transformed data and linear-scale data, having different patterns of input information can enhance the prediction performance of the EM inversion results.

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