Abstract. This study introduces an efficient deep-learning model based on convolutional
neural networks with joint autoencoder and adversarial structures for 3D
subsurface mapping from 2D surface observations. The method was applied to
delineate paleovalleys in an Australian desert landscape. The neural network
was trained on a 6400 km2 domain by using a land surface topography
as 2D input and an airborne electromagnetic (AEM)-derived probability map of
paleovalley presence as 3D output. The trained neural network has a squared
error <0.10 across 99 % of the training domain and produces a
squared error <0.10 across 93 % of the validation domain,
demonstrating that it is reliable in reconstructing 3D paleovalley patterns
beyond the training area. Due to its generic structure, the neural network
structure designed in this study and the training algorithm have broad
application potential to construct 3D geological features (e.g., ore bodies,
aquifer) from 2D land surface observations.