Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion
Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies. Recovering a three-dimensional density model from gravity observations is highly ill-posed because of its non-uniqueness, limited data coverage, and the attenuation of the gravity field with depth. Classical inversion methods rely on explicit regularization and parameter tuning, whereas supervised deep-learning approaches require representative gravity--density pairs that are rarely available. This paper proposes an unsupervised depth-aware implicit neural representation for 3D gravity inversion. The density volume is represented by multiple coordinate-based neur...
| Authors | León Suarez-Rodriguez, Paul Goyes-Peñafiel, Javier Torres-Quintero et al. |
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| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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