Three-dimensional inversion for short-offset transient electromagnetic data based on 3D U-Net

Abstract The short-offset transient electromagnetic (SOTEM) method carries out surveys in the near source region; the strong signal makes it suitable for deep detection with high precision. When the underground structure is complex, three-dimensional (3D) inversion of SOTEM data is necessary to meet the need of high-precision detection. Currently, difficulties faced by the conventional 3D inversion methods include high computational complexity, and the influence of the initial model. Deep learning (DL), as a completely nonlinear algorithm, can predict the underground structure from the measured data. DL is completely data-driven and does not use traditional misfit optimization methods. In this study, an efficient method is proposed to conduct 3D inversion for SOTEM data, which trains a 3D U-Net based on massive data to establish a mapping from SOTEM data to geoelectric models. After the training is completed, we input the new SOTEM data into the trained network, and the corresponding geoelectric model can be obtained. Although the training is a time-consuming process, prediction for new data can be completed in seconds. The inversion results for simulated data indicate that the 3D U-Net has good generalization performance and anti-noise ability. The inversion performance of 3D U-Net on the double-anomaly model improves by 51.1% compared to a 3D fully convolutional network (FCN). The inversion results of the 3D U-Net on the field data successfully delineated the aquiferous collapse column. The inversion results for simulated and field data demonstrate that the proposed method can achieve accurate 3D inversion for large volume of data while greatly saving computational time.