DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling
Abstract: In order to reconcile petrological and geophysical observations of magmatic processes in the temporal domain, the uncertainties in diffusion timescales need to be rigorously assessed. Here, we present a new diffusion chronometry method: Diffusion chronometry using Finite Elements and Neste...
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ftunivcam:oai:www.repository.cam.ac.uk:1810/321466 2023-10-29T02:37:19+01:00 DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling Mutch, Euan J. F. Maclennan, John Shorttle, Oliver Rudge, John F. Neave, David A. 2021-04-22T23:19:36Z application/pdf text/xml https://www.repository.cam.ac.uk/handle/1810/321466 https://doi.org/10.17863/CAM.68586 en eng https://www.repository.cam.ac.uk/handle/1810/321466 doi:10.17863/CAM.68586 Research Article Bárðarbunga Bayesian inversion diffusion modeling finite elements Iceland volcanic and magmatic timescales olivine and plagioclase geospeedometry Skuggafjöll Article 2021 ftunivcam https://doi.org/10.17863/CAM.68586 2023-10-05T22:20:35Z Abstract: In order to reconcile petrological and geophysical observations of magmatic processes in the temporal domain, the uncertainties in diffusion timescales need to be rigorously assessed. Here, we present a new diffusion chronometry method: Diffusion chronometry using Finite Elements and Nested Sampling (DFENS). This method combines a finite element numerical model with a nested sampling Bayesian inversion, meaning that uncertainties in the parameters contributing to diffusion timescale estimates can be obtained and that observations from multiple elements can be used to better constrain individual timescales. Uncertainties associated with diffusion timescales can be reduced by accounting for covariance in the uncertainty structure of diffusion parameters rather than assuming that they are independent of each other. We applied the DFENS method to the products of the Skuggafjöll eruption from the Bárðarbunga volcanic system in Iceland, which contains zoned macrocrysts of olivine and plagioclase that record a shared magmatic history. Olivine and plagioclase provide consistent pre‐eruptive mixing and mush disaggregation timescales of less than 1 year. The DFENS method goes some way toward improving our ability to rigorously address the uncertainties of diffusion timescales, but efforts still need to be made to understand other systematic sources of uncertainty such as crystal morphology, appropriate choice of diffusion coefficients, initial conditions, crystal growth, and the petrological context of diffusion timescales. Article in Journal/Newspaper Iceland Apollo - University of Cambridge Repository |
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Apollo - University of Cambridge Repository |
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English |
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Research Article Bárðarbunga Bayesian inversion diffusion modeling finite elements Iceland volcanic and magmatic timescales olivine and plagioclase geospeedometry Skuggafjöll |
spellingShingle |
Research Article Bárðarbunga Bayesian inversion diffusion modeling finite elements Iceland volcanic and magmatic timescales olivine and plagioclase geospeedometry Skuggafjöll Mutch, Euan J. F. Maclennan, John Shorttle, Oliver Rudge, John F. Neave, David A. DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling |
topic_facet |
Research Article Bárðarbunga Bayesian inversion diffusion modeling finite elements Iceland volcanic and magmatic timescales olivine and plagioclase geospeedometry Skuggafjöll |
description |
Abstract: In order to reconcile petrological and geophysical observations of magmatic processes in the temporal domain, the uncertainties in diffusion timescales need to be rigorously assessed. Here, we present a new diffusion chronometry method: Diffusion chronometry using Finite Elements and Nested Sampling (DFENS). This method combines a finite element numerical model with a nested sampling Bayesian inversion, meaning that uncertainties in the parameters contributing to diffusion timescale estimates can be obtained and that observations from multiple elements can be used to better constrain individual timescales. Uncertainties associated with diffusion timescales can be reduced by accounting for covariance in the uncertainty structure of diffusion parameters rather than assuming that they are independent of each other. We applied the DFENS method to the products of the Skuggafjöll eruption from the Bárðarbunga volcanic system in Iceland, which contains zoned macrocrysts of olivine and plagioclase that record a shared magmatic history. Olivine and plagioclase provide consistent pre‐eruptive mixing and mush disaggregation timescales of less than 1 year. The DFENS method goes some way toward improving our ability to rigorously address the uncertainties of diffusion timescales, but efforts still need to be made to understand other systematic sources of uncertainty such as crystal morphology, appropriate choice of diffusion coefficients, initial conditions, crystal growth, and the petrological context of diffusion timescales. |
format |
Article in Journal/Newspaper |
author |
Mutch, Euan J. F. Maclennan, John Shorttle, Oliver Rudge, John F. Neave, David A. |
author_facet |
Mutch, Euan J. F. Maclennan, John Shorttle, Oliver Rudge, John F. Neave, David A. |
author_sort |
Mutch, Euan J. F. |
title |
DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling |
title_short |
DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling |
title_full |
DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling |
title_fullStr |
DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling |
title_full_unstemmed |
DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling |
title_sort |
dfens: diffusion chronometry using finite elements and nested sampling |
publishDate |
2021 |
url |
https://www.repository.cam.ac.uk/handle/1810/321466 https://doi.org/10.17863/CAM.68586 |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
https://www.repository.cam.ac.uk/handle/1810/321466 doi:10.17863/CAM.68586 |
op_doi |
https://doi.org/10.17863/CAM.68586 |
_version_ |
1781061932276514816 |