Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach
JGR Machine Learning and Computation
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- More information: Publisher Index Page (via DOI)
- Data Releases:
- USGS data release - spheroid90gp: Gaussian process emulation of vertical spheroidal elastic cavity models
- USGS data release - Trained emulators from the spheroid90gp software package
- Open Access Version: Publisher Index Page
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Abstract
Elastic continuum mechanical models are widely used to compute deformations due to pressure changes in buried cavities, such as magma reservoirs. In general, analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software. To overcome these limitations, we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity but >1,000× greater computational efficiency. The emulators are based on generalized nondimensional forms of governing equations for finite non‐dipping spheroidal cavities in elastic halfspaces. Either cavity volume change or uniform pressure change boundary conditions can be specified, and the models predict both surface displacements and cavity (pore) compressibility. Because of their computational efficiency, using the emulators as numerical model surrogates can greatly accelerate data inversion algorithms such as those employing Bayesian Markov chain Monte Carlo sampling. The emulators also permit a comprehensive evaluation of how displacements and cavity compressibility vary with geometry and material properties, revealing the limitations of analytical models. Our open‐source emulator code can be utilized without finite element software, is suitable for a wide range of cavity geometries and depths, includes an estimate of uncertainties associated with emulation, and can be used to train new emulators for different source geometries.
Suggested Citation
Anderson, K.R., Gu, M., 2024, Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach: Journal of Geophysical Research: Machine Learning and Computation, v. 1, e2024JH000161, 20 p., https://doi.org/10.1029/2024JH000161.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach |
| Series title | JGR Machine Learning and Computation |
| DOI | 10.1029/2024JH000161 |
| Volume | 1 |
| Publication Date | July 20, 2024 |
| Year Published | 2024 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Volcano Science Center |
| Description | e2024JH000161, 20 p. |