Deep Learning in Geophysical Electromagnetic Inversion: A review

Innovative inversion methods are pivotal for optimizing imaging and interpretation in geophysical exploration. Deep learning (DL) has emerged as a transformative solution, leveraging hierarchical abstractions to overcome the efficiency bottlenecks of traditional numerical methods. Recent studies demonstrate that DL accelerates computational workflows by orders of magnitude—achieving speedups of 200‒350×, particularly in 3D forward modeling tasks—while maintaining high reconstruction accuracy (>95% consistency with true models) for specific structured inversion scenarios. This success has fostered a data-driven paradigm that complements classical physics-based optimization. This review article systematically summarizes the latest advancements in DL-based geophysical electromagnetic (EM) inversion. We delineate the algorithmic foundations and detail core application strategies addressing critical challenges, including computational efficiency, complex medium imaging, and the integration of physical laws. Finally, we critically assess current limitations and propose a prioritized research agenda for the future of intelligent inversion.