Surf3DNet1.0: A deep learning model for regional-scale 3D subsurface structure mapping

Abstract

Abstract. This study introduces an efficient deep learning approach based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from surface observations. The method was applied to delineate palaeovalleys in an Australian desert landscape. The neural network was trained on a 6,400 km2 domain by using a land surface tomography as 2D input and an airborne electromagnetic (AEM)-derived probability map of palaeovalley presence as 3D output. The trained neural network has a maximum square error