Magnetic Inversion

Magnetic inversion aims to reconstruct subsurface properties from surface measurements of geophysical fields, such as magnetic anomalies or seismic waves. Recent research focuses on improving inversion accuracy and efficiency using deep learning, particularly through self-supervised and semi-supervised approaches that reduce reliance on extensive labeled data. These methods, employing architectures like knowledge-driven neural networks and techniques like multi-dimensional sample generation, address the ill-posed nature of the problem and enable high-resolution imaging even with limited data. This leads to more robust and reliable subsurface characterization with applications in resource exploration, environmental monitoring, and infrastructure assessment.