Deep joint inversion of multiple geophysical data with U-net reparameterization

Joint inversion integrates a variety of geophysical data in a unified framework to reveal different subsurface properties. Existing methods often require predefined correlations through mathematical equations or training data sets, heavily relying on prior information. In addition, integrating multiphysics data with different sensitivities is challenging. To address these limitations, we develop a novel approach that uses U-net to autonomously establish correlations during inversion, which yields consistent inverted models across different resolutions. Different properties are represented by U-net parameters and solved by minimizing multimodality data misfits simultaneously. We validate our approach through structure-constrained electromagnetic inversion, joint seismic and gravity inversion, and joint electromagnetic, seismic, and gravity inversion with synthetic data. This study affirms the feasibility of leveraging machine intelligence to autonomously integrate multiple geophysical data for precise subsurface characterization.