Deep learning transient electromagnetic inversion for seawater intrusion

Abstract To enhance the capability of the transient electromagnetic method (TEM) in detecting seawater intrusion and delineating the boundaries in coastal areas, we developed a deep learning inversion method for TEM data based on the Swin Transformer model in this study. First many standardized resistivity models were designed and generated to describe the subsurface resistivity structures associated with seawater intrusion in coastal areas. Then, TEM forward modeling was performed to compute the corresponding TEM responses, thereby constructing a seawater intrusion-oriented training dataset. Next, the robust Swin Transformer model was employed as the backbone network to build a deep learning inversion model, named SITEMNet, to derive a direct nonlinear transformation that maps TEM responses to subsurface resistivity models. The proposed SITEMNet inversion technique was validated using simulated data scenarios and actual field TEM measurements, showing great promise in accurately identifying seawater intrusion interface and geological formations.