Deep learning-based approach to Levelling Airborne Magnetic Data

Airborne magnetic surveys provide valuable insights into subsurface structures but often suffer from levelling errors due to inconsistencies between flight lines. These errors, such as striping patterns caused by sensor variations and magnetic field fluctuations, can obscure anomalies and distort interpretations. Traditional corrections like tie-line or micro levelling address these issues but rely on time and frequency domain analyses, making the process labor-intensive, costly, and reliant on expert intervention. Automating and enhancing these workflows is crucial for efficient and accurate levelling across large-scale airborne magnetic datasets. In this work, we propose a deep learning framework for levelling airborne magnetic data by leveraging a U-Net-based architecture. The model is trained in a supervised manner. We use a combination of perceptual loss and mean squared error (MSE) loss to capture fine-grained details while maintaining global consistency in the levelled data. Once trained, the proposed method demonstrates computational efficiency during inference, enabling automatic and robust levelling corrections for large datasets without requiring manual intervention or additional tie-line constraints. The model's performance was evaluated on an independent survey data from the Geological Survey of Brazil database, as well as on an out-of-distribution (OOD) dataset consisting of magnetic field data acquired by Geotech Limited, demonstrating its generalizability and robustness. Our approach demonstrates performance on par with traditional levelling methods, as validated by both quantitative and qualitative metrics, while introducing significant advantages in efficiency and automation. This deep learning-based solution simplifies the levelling process and provides a scalable, adaptive framework designed to meet the demands of modern geophysical surveys.