Orebody-Oriented Electromagnetic Inversion via Gravity-Guided Neural Networks

Certain ore deposits feature both density and electrical anomalies, making them detectable via gravity and electromagnetic (EM) methods. However, under complex field conditions, signals are often distorted or lost due to observational errors, undermining inversion reliability. In joint inversion, errors from a single data source may mislead the overall model, resulting in structural deviations and blurred orebody boundaries. Additionally, gravity and EM inversions exhibit different volume effects, often causing inconsistencies in spatial scale representation. Their distinct physical mechanisms further hinder the establishment of clear nonlinear mappings, limiting the effectiveness of traditional joint inversion approaches in achieving consistent integration and stable results. To address these challenges, we propose a Spatial Density-Informed Electromagnetic Inversion Network (SDI-EMI Network), a deep inversion network that fuses spatial density and EM response data. The network first performs gravity inversion to estimate orebody geometry, which serves as a structural prior for guiding EM inversion. By aligning volume deformation patterns, it ensures unified scale representation, enhances data complementarity, and suppresses interference from non-orebody regions. This method overcomes limitations in prior modeling and data fusion while leveraging deep learning’s nonlinear capacity. Experimental results confirm that SDI-EMI Network. offers improved resolution, structural clarity, and robustness for identifying deposits with coexisting density and resistivity anomalies, supporting its potential in complex geological settings.