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

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 effectively captures strong geometric relationships and structural continuity within 3D distributions. Furthermore, we introduce a transceiver altitude modulation mechanism, enabling the network to accurately handle variations in forward modeling induced by changes in transceiver altitude. Comprehensive experiments demonstrate that the proposed method achieves fast and high‐precision 3D ATEM forward modeling, exhibits sensitivity to transceiver altitude, and shows applicability to realistic geoelectrical models.