Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference
Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it in the absence of retrospective observations remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure...
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ftdatacite:10.48550/arxiv.2006.12422 2023-05-15T13:47:43+02:00 Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference Brinkerhoff, Douglas Aschwanden, Andy Fahnestock, Mark 2020 https://dx.doi.org/10.48550/arxiv.2006.12422 https://arxiv.org/abs/2006.12422 unknown arXiv https://dx.doi.org/10.1017/jog.2020.112 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computational Physics physics.comp-ph Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an Geophysics physics.geo-ph FOS Physical sciences FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2020 ftdatacite https://doi.org/10.48550/arxiv.2006.12422 https://doi.org/10.1017/jog.2020.112 2022-03-10T15:30:53Z Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it in the absence of retrospective observations remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, classical MCMC sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development. Article in Journal/Newspaper Antarc* Antarctica Greenland DataCite Metadata Store (German National Library of Science and Technology) Greenland |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Computational Physics physics.comp-ph Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an Geophysics physics.geo-ph FOS Physical sciences FOS Computer and information sciences |
spellingShingle |
Computational Physics physics.comp-ph Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an Geophysics physics.geo-ph FOS Physical sciences FOS Computer and information sciences Brinkerhoff, Douglas Aschwanden, Andy Fahnestock, Mark Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference |
topic_facet |
Computational Physics physics.comp-ph Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an Geophysics physics.geo-ph FOS Physical sciences FOS Computer and information sciences |
description |
Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it in the absence of retrospective observations remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, classical MCMC sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development. |
format |
Article in Journal/Newspaper |
author |
Brinkerhoff, Douglas Aschwanden, Andy Fahnestock, Mark |
author_facet |
Brinkerhoff, Douglas Aschwanden, Andy Fahnestock, Mark |
author_sort |
Brinkerhoff, Douglas |
title |
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference |
title_short |
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference |
title_full |
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference |
title_fullStr |
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference |
title_full_unstemmed |
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference |
title_sort |
constraining subglacial processes from surface velocity observations using surrogate-based bayesian inference |
publisher |
arXiv |
publishDate |
2020 |
url |
https://dx.doi.org/10.48550/arxiv.2006.12422 https://arxiv.org/abs/2006.12422 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Antarc* Antarctica Greenland |
genre_facet |
Antarc* Antarctica Greenland |
op_relation |
https://dx.doi.org/10.1017/jog.2020.112 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2006.12422 https://doi.org/10.1017/jog.2020.112 |
_version_ |
1766247748037771264 |