Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries Article Swipe
Taiyi Wang
,
Ian W. McBrearty
,
P. Segall
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.30909/vol.08.01.95109
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.30909/vol.08.01.95109
Measurements of volcano deformation are increasingly routine, but constraining complex magma reservoir geometries via inversions of surface deformation measurements remains challenging. This is partly due to deformation modeling being limited to one of two approaches: computationally efficient semi-analytical elastic solutions for simple magma reservoir geometries (point sources, spheroids, and cracks) and computationally expensive numerical solutions for complex 3D geometries. Here, we introduce a pair of Graph Neural Network (GNN) based elasto-static emulators capable of making fast and reasonably accurate predictions (error upper bound: 15 %) of surface deformation associated with 3D reservoir geometries: a spheroid emulator and a general shape emulator, the latter parameterized with spherical harmonics. The emulators are trained on, and benchmarked against, boundary element (BEM) simulations, providing up to three orders of magnitude speed up compared to BEM methods. Once trained, the emulators can generalize to new reservoir geometries statistically similar to those in the training data set, thus avoiding the need for re-training, a common limitation for existing neural network emulators. We demonstrate the utility of the emulators via Bayesian Markov Chain Monte Carlo inversions of synthetic surface deformation data, showcasing scenarios in which the emulators can, and can not, resolve complex magma reservoir geometries from surface deformation. Our work demonstrates that GNN based emulators have the potential to significantly reduce the computational cost of inverse analyses related to volcano deformation, thereby bringing new insights into the complex geometries of magmatic systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.30909/vol.08.01.95109
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407831189
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https://openalex.org/W4407831189Canonical identifier for this work in OpenAlex
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https://doi.org/10.30909/vol.08.01.95109Digital Object Identifier
- Title
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Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometriesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-02-21Full publication date if available
- Authors
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Taiyi Wang, Ian W. McBrearty, P. SegallList of authors in order
- Landing page
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https://doi.org/10.30909/vol.08.01.95109Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.30909/vol.08.01.95109Direct OA link when available
- Concepts
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Artificial neural network, Deformation (meteorology), Graph, Computer science, Geology, Artificial intelligence, Theoretical computer science, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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