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Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt

The mass transfer history of rocks provides direct evidence for fluid–rock interaction within the lithosphere and is recorded by compositional changes, especially in trace elements. The general method adopted for mass transfer analysis is to compare the composition of the protolith/precursor with th...

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Autores principales: Matsuno, Satoshi, Uno, Masaoki, Okamoto, Atsushi, Tsuchiya, Noriyoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791977/
https://www.ncbi.nlm.nih.gov/pubmed/35082334
http://dx.doi.org/10.1038/s41598-022-05109-x
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author Matsuno, Satoshi
Uno, Masaoki
Okamoto, Atsushi
Tsuchiya, Noriyoshi
author_facet Matsuno, Satoshi
Uno, Masaoki
Okamoto, Atsushi
Tsuchiya, Noriyoshi
author_sort Matsuno, Satoshi
collection PubMed
description The mass transfer history of rocks provides direct evidence for fluid–rock interaction within the lithosphere and is recorded by compositional changes, especially in trace elements. The general method adopted for mass transfer analysis is to compare the composition of the protolith/precursor with that of metamorphosed/altered rocks; however, in many cases the protolith cannot be sampled. With the aim of reconstructing the mass transfer history of metabasalt, this study developed protolith reconstruction models (PRMs) for metabasalt using machine-learning algorithms. We designed models to estimate basalt trace-element concentrations from the concentrations of a few (1–9) trace elements, trained with a compositional dataset for fresh basalts, including mid-ocean ridge, ocean-island, and volcanic arc basalts. The developed PRMs were able to estimate basalt trace-element compositions (e.g., Rb, Ba, U, K, Pb, Sr, and rare-earth elements) from only four input elements with a reproducibility of ~ 0.1 log(10) units (i.e., ± 25%). As a representative example, we present PRMs where the input elements are Th, Nb, Zr, and Ti, which are typically immobile during metamorphism. Case studies demonstrate the applicability of PRMs to seafloor altered basalt and metabasalt. This method enables us to analyze quantitative mass transfer in regional metamorphic rocks or alteration zones where the protolith is heterogeneous or unknown.
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spelling pubmed-87919772022-01-27 Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt Matsuno, Satoshi Uno, Masaoki Okamoto, Atsushi Tsuchiya, Noriyoshi Sci Rep Article The mass transfer history of rocks provides direct evidence for fluid–rock interaction within the lithosphere and is recorded by compositional changes, especially in trace elements. The general method adopted for mass transfer analysis is to compare the composition of the protolith/precursor with that of metamorphosed/altered rocks; however, in many cases the protolith cannot be sampled. With the aim of reconstructing the mass transfer history of metabasalt, this study developed protolith reconstruction models (PRMs) for metabasalt using machine-learning algorithms. We designed models to estimate basalt trace-element concentrations from the concentrations of a few (1–9) trace elements, trained with a compositional dataset for fresh basalts, including mid-ocean ridge, ocean-island, and volcanic arc basalts. The developed PRMs were able to estimate basalt trace-element compositions (e.g., Rb, Ba, U, K, Pb, Sr, and rare-earth elements) from only four input elements with a reproducibility of ~ 0.1 log(10) units (i.e., ± 25%). As a representative example, we present PRMs where the input elements are Th, Nb, Zr, and Ti, which are typically immobile during metamorphism. Case studies demonstrate the applicability of PRMs to seafloor altered basalt and metabasalt. This method enables us to analyze quantitative mass transfer in regional metamorphic rocks or alteration zones where the protolith is heterogeneous or unknown. Nature Publishing Group UK 2022-01-26 /pmc/articles/PMC8791977/ /pubmed/35082334 http://dx.doi.org/10.1038/s41598-022-05109-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Matsuno, Satoshi
Uno, Masaoki
Okamoto, Atsushi
Tsuchiya, Noriyoshi
Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
title Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
title_full Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
title_fullStr Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
title_full_unstemmed Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
title_short Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
title_sort machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791977/
https://www.ncbi.nlm.nih.gov/pubmed/35082334
http://dx.doi.org/10.1038/s41598-022-05109-x
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