{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "70256116",
"url": "https://pubs.usgs.gov/publication/70256116"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70256116
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.1029/2024JH000161",
"url": "https://doi.org/10.1029/2024JH000161"
}
],
"journal": {
"@type": "Periodical",
"name": "Journal of Geophysical Research: Machine Learning and Computation",
"volumeNumber": "1",
"issueNumber": null
},
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Journal of Geophysical Research: Machine Learning and Computation"
}
],
"datePublished": "2024",
"dateModified": "2024-07-23",
"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\u00d7 greater computational efficiency. The emulators are based on generalized nondimensional forms of governing equations for finite non\u2010dipping 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\u2010source 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.",
"description": "e2024JH000161, 20 p.",
"publisher": {
"@type": "Organization",
"name": "Wiley"
},
"author": [
{
"@type": "Person",
"name": "Anderson, Kyle R. kranderson@usgs.gov",
"givenName": "Kyle R.",
"familyName": "Anderson",
"email": "kranderson@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-8041-3996",
"url": "https://orcid.org/0000-0001-8041-3996"
},
"affiliation": [
{
"@type": "Organization",
"name": "Volcano Science Center",
"url": "https://www.usgs.gov/centers/volcano-science-center"
}
]
},
{
"@type": "Person",
"name": "Gu, Mengyang",
"givenName": "Mengyang",
"familyName": "Gu",
"affiliation": [
{
"@type": "Organization",
"name": "U.C. Santa Barbara"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "Volcano Science Center",
"url": "https://www.usgs.gov/centers/volcano-science-center"
}
]
}
}