[2510.17876] Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learning

Physics > Geophysics arXiv:2510.17876 (physics) [Submitted on 17 Oct 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learning Authors:Pankaj K Mishra, Sanni Laaksonen, Jochen Kamm, Anand Singh View a PDF of the paper titled Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learning, by Pankaj K Mishra and 2 other authors View PDF HTML (experimental) Abstract:Inversion of gravity data is an important method for investigating subsurface density variations relevant to mineral exploration, geothermal assessment, carbon storage, natural hydrogen, groundwater resources, and tectonic evolution. Here we present a scientific machine-learning approach for three-dimensional gravity inversion that represents subsurface density as a continuous field using an implicit neural representation (INR). The method trains a deep neural network directly through a physics-based forward-model loss, mapping spatial coordinates to a continuous density field without predefined meshes or discretisation. Spatial encoding enhances the network's capacity to capture sharp contrasts and short-wavelength features that conventional coordinate-based networks tend to oversmooth due to spectral bias. We demonstrate the approach on synthetic examples including smooth models, representing realistic geological complexity, and a dipping block mo...