Rapid and High‐Accuracy Three‐Dimensional Airborne Transient Electromagnetic Forward Modeling Based on Machine Learning

Abstract Conventional numerical methods for computing three‐dimensional (3D) airborne transient electromagnetic (ATEM) forward modeling suffer from low computational efficiency and high computational cost. Although deep learning techniques have achieved some progress in accelerating low‐dimensional ATEM forward modeling, high‐accuracy and efficient 3D forward modeling has yet to be realized. To address this gap, we propose a deep learning–based approach for rapid and accurate 3D ATEM forward modeling. To simulate complex and heterogeneous geological conditions, we construct a large‐scale multi‐structure data set that incorporates most common subsurface features, including folds and faults. To address the extremely high computational cost required by transformers when processing 3D data, as well as the limitation of convolutional networks in globally modeling geoelectric structures—given that 3D ATEM forward modeling requires consideration of the entire model—we develop a network architecture based on the receptance weighted key value (RWKV). By employing bidirectional quadratic expansion and bidirectional weighted key value (Bi‐WKV) operations, the network effecti