2025/06/24 by Nanyu Wei, Dikun Yang · 1 voice
Earth and Planetary Sciences · Engineering · Environmental Science · #Geophysical Methods and Applications #Geophysical and Geoelectrical Methods #Methane Hydrates and Related Phenomena
paper · doi:10.1016/j.bdes.2025.100005
openalex publication_date 2025/06/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Traditional inversion methods often struggle to integrate data of different types, scales, and physical fields, leading to information loss and under-constrained models. This study proposes MP3D-NET, a deep learning-based multi-physics 3D joint inversion framework that brings the concept of multimodal fusion in machine learning to geophysical joint inversion. MP3D-NET enables joint inversion of any number of 1D, 2D, and 3D geophysical datasets, improving the stability and flexibility of handling multi-physical data. We validated the framework using synthetic datasets (block, ellipsoid, and tetrahedron) and further tested its applicability on a realistic Kimberlite exploration model. The results demonstrate that MP3D-NET effectively integrates diverse geophysical data, enhances subsurface geometric reconstructions, and optimizes inversion results through data misfit tests and the incorporation of prior information. The architecture of MP3D-NET is designed to process and integrate 1D, 2D, and 3D geophysical input data in parallel. Each data type is first processed through its own modality-specific channel, and then combined using a shared activation function. By leveraging the nonlinear mapping capability of deep neural networks, MP3D-NET directly predicts the probability of mineralization from geophysical data. Unlike traditional joint inversion methods that first reconstruct multiple physical properties models before geological interpretation, MP3D-NET embeds these relationships within the network, allowing direct geological interpretation without explicit physical model reconstruction. This study establishes a theoretical foundation for multi-modal learning in geophysical joint inversion, providing new perspectives for adopting the latest artificial intelligence technology into geophysical data processing.