Aeromagnetic Methods for Mineral Exploration in Complex Geological Settings: Advances in Technology, Data Processing, and Multidisciplinary Integration-A Review

Abstract

Aeromagnetic methods are critical for mineral exploration in geologically complex regions. This review evaluates recent advancements in sensor technology, data processing algorithms, and multidisciplinary integration to overcome challenges such as noise interference, depth constraints, and structural ambiguities, aiming to enhance exploration efficacy and sustainability. The study analyses high-sensitivity cesium vapour magnetometers (±0.052nT), UAV systems for high-resolution surveys, and noise reduction techniques like multifractal singular value decomposition (MSVD) and improved bi-dimensional empirical mode decomposition (BEMD). Advanced 3D inversion models and integration with gravity, gamma-ray, and machine learning are assessed. Case studies from Nigeria’s mineral-rich regions, China’s 27,000 km² Nanpanjiang-Youjiang belt survey, and Korea’s UAV-based iron ore detection illustrate methodological adaptations. These methods successfully identified concealed faults and mineralized zones, with UAV surveys achieving 5 m resolution. Noise reduction improved anomaly detection by 30%, while AI-driven analysis reduced exploration risks by prioritizing high-potential targets. However, depth penetration beyond 2 km and residual noise in highly magnetic regions remain limitations. As demonstrated in Nigeria's subsurface deposit mapping, integrated approaches reduced interpretation uncertainties by 40%. Aeromagnetic techniques are indispensable for sustainable mineral exploration in challenging terrains. Future advancements should focus on AI-enhanced data fusion, quantum sensor technology for deeper targets, and environmentally low-impact UAV deployments. Collaborative frameworks combining aeromagnetics with remote sensing and deep learning are recommended to optimize resource discovery while balancing economic and ecological priorities.