Encoding physics to learn marine self-potential: application to seafloor sulfide exploration

Summary The Marine Self-Potential (SP) method has proven to be a useful geophysical tool for localizing seafloor massive sulfide (SMS) deposits. Conventional geophysical inversion approaches face significant limitations in mid-ocean ridge environments with complex topography, including high computational requirements due to high degrees of freedom in grid discretization and the complex computation of sensitivities. Meanwhile, data-driven neural networks, while powerful, often suffer from poor generalizability and interpretability in complex deep-sea environments, owing to their reliance on large-scale training datasets. In this study, we propose a simple-formulated intelligent SP inversion framework, called SP-PINN, based on the Physics-Informed Neural Network (PINN). By embedding the governing equation of SP as a physical constraint into the loss function, SP-PINN effectively couples the intrinsic correlation between SP signals and subseafloor causative sources. To address the characteristics of topographic relief in mid-ocean ridges and discrete distribution of SMS ore bodies, 2D and 3D synthetic case studies were constructed to verify the inversion performance of SP-PINN. Two SMS deposit bodies on the Southwest Indian Ridge (SWIR) were successfully imaged and delineated through the application of SP-PINN to the field data. Our results demonstrate that accurate imaging of SMS can be achieved by introducing physical constraint terms into the neural network, properly optimizing hyperparameters, and incorporating a small amount of field data. This study provides a straightforward and physically interpretable SP imaging method for the assessment of SMS under data-scarce conditions.