Multimodal Geophysics-Informed Neural Network for Joint Inversion of Seismic Electromagnetic and Well-logging

Geophysical joint inversion can comprehensively utilize the sensitivity of different geophysical data to various physical parameters to reduce the multi-solution comparing for single inversion method. To address the challenges of modality differences in geophysical observation data and the limitations of existing joint inversion methods in achieving high-resolution prediction and fine-scale evaluations of underground resources, we propose a multi-modal geophysics-informed neural network designed for the joint inversion of seismic, electromagnetic, and well-logging data. The proposed method integrates physical information into the neural network architecture from three key aspects: network structure, data constraints, and loss functions. It achieves multi-modal data fusion through feature maps concatenation and gradient backpropagation, complemented by a multi-stage optimization process that enhances the accuracy of inversion results. Comparative experimental results demonstrate that our method produces inversion results with superior spatial continuity and the ability to resolve finer geological structures. It can offer massive data support for subsequent underground resource extraction efforts.