Joint 3D Gravity and Magnetic Inversion via Rectified Flow and Ginzburg-Landau Guidance
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911495164526592 |
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| author | Gupta, Dhruman Shende, Yashas Das, Aritra Kamra, Chanda Grover Gupta, Debayan |
| author_facet | Gupta, Dhruman Shende, Yashas Das, Aritra Kamra, Chanda Grover Gupta, Debayan |
| contents | Subsurface ore detection is of paramount importance given the gradual depletion of shallow mineral resources in recent years. It is crucial to explore approaches that go beyond the limitations of traditional geological exploration methods. One such promising new method is joint magnetic and gravitational inversion. Given magnetic and gravitational data on a surface, jointly reconstructing the underlying densities that generate them remains an ill-posed inverse problem. Although joint inversion of multiple properties mitigates the non-uniqueness problem in magnetic and gravitational data, deterministic algorithms converge to a single regularized solution and thus do not capture the distribution of possible solutions. Similarly, most machine learning based techniques predict a single solution without modelling the entire distribution. In this paper, we introduce a novel framework that reframes 3D gravity and magnetic joint inversion as a rectified flow on the Noddyverse dataset, the largest physics-based dataset for inversion. We introduce a Ginzburg-Landau (GL) regularizer, a generalized version of the Ising model that aids in ore identification, enabling physics-aware training. We also propose a guidance methodology based on GL theory that can be used as a plug-and-play module with existing unconditional denoisers. Lastly, we also train and release a VAE for the 3D densities, which facilitates downstream work in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06829 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Joint 3D Gravity and Magnetic Inversion via Rectified Flow and Ginzburg-Landau Guidance Gupta, Dhruman Shende, Yashas Das, Aritra Kamra, Chanda Grover Gupta, Debayan Machine Learning Subsurface ore detection is of paramount importance given the gradual depletion of shallow mineral resources in recent years. It is crucial to explore approaches that go beyond the limitations of traditional geological exploration methods. One such promising new method is joint magnetic and gravitational inversion. Given magnetic and gravitational data on a surface, jointly reconstructing the underlying densities that generate them remains an ill-posed inverse problem. Although joint inversion of multiple properties mitigates the non-uniqueness problem in magnetic and gravitational data, deterministic algorithms converge to a single regularized solution and thus do not capture the distribution of possible solutions. Similarly, most machine learning based techniques predict a single solution without modelling the entire distribution. In this paper, we introduce a novel framework that reframes 3D gravity and magnetic joint inversion as a rectified flow on the Noddyverse dataset, the largest physics-based dataset for inversion. We introduce a Ginzburg-Landau (GL) regularizer, a generalized version of the Ising model that aids in ore identification, enabling physics-aware training. We also propose a guidance methodology based on GL theory that can be used as a plug-and-play module with existing unconditional denoisers. Lastly, we also train and release a VAE for the 3D densities, which facilitates downstream work in the field. |
| title | Joint 3D Gravity and Magnetic Inversion via Rectified Flow and Ginzburg-Landau Guidance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.06829 |