This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Masaoki Uno
Tohoku University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Mass transfer in rocks provides a direct record of fluid–rock interaction within the Earth, including metamorphism, metasomatism, and hydrothermal alteration. However, mass transfer analyses are usually limited to local reaction zones where the protoliths are evident in outcrops (1–100 m in scale), from which regional mass transfer can be only loosely constrained due to uncertainty in protolith compositions. In this study, we developed protolith reconstruction models (PRMs) for metabasalt based on a machine learning approach. We constructed PRMs through learning multi-element correlations among basalt compositional datasets, including mid-ocean ridge, ocean island, and island arc basalts. The PRMs were designed to estimate trace-element compositions from inputs of 2–9 selected trace elements, and basalt trace-element compositions (e.g., Rb, Ba, U, K, Pb, Sr, and rare earth elements) were estimated from only four inputs with a reproducibility of ~0.1 log10 units (i.e., ±25%). Using Th, Nb, Zr, and Ti, which are typically immobile during metamorphism, as input elements, the PRM was verified by applying it to seafloor altered basalt with known protoliths. When suitable immobile elements are incorporated, a PRM can yield unbiased and accurate mass transfer analysis of any metabasalt with unknown protolith.
Keywords
fluid–rock interaction, Earth, including metamorphism, metasomatism, PRMs
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 28 May, 2021
No community comments so far
Editorial decision: Major revision
On 17 Aug, 2021
Reviews received
Received 09 Aug, 2021
Reviews received
Received 24 Jul, 2021
Reviewers agreed
On 19 Jul, 2021
Reviewers invited
Invitations sent on 25 Jun, 2021
Editor assigned
On 25 Jun, 2021
Editor invited
On 27 May, 2021
Submission checks complete
On 26 May, 2021
First submitted
On 25 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Masaoki Uno
Tohoku University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 28 May, 2021
No community comments so far
Editorial decision: Major revision
On 17 Aug, 2021
Reviews received
Received 09 Aug, 2021
Reviews received
Received 24 Jul, 2021
Reviewers agreed
On 19 Jul, 2021
Reviewers invited
Invitations sent on 25 Jun, 2021
Editor assigned
On 25 Jun, 2021
Editor invited
On 27 May, 2021
Submission checks complete
On 26 May, 2021
First submitted
On 25 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Mass transfer in rocks provides a direct record of fluid–rock interaction within the Earth, including metamorphism, metasomatism, and hydrothermal alteration. However, mass transfer analyses are usually limited to local reaction zones where the protoliths are evident in outcrops (1–100 m in scale), from which regional mass transfer can be only loosely constrained due to uncertainty in protolith compositions. In this study, we developed protolith reconstruction models (PRMs) for metabasalt based on a machine learning approach. We constructed PRMs through learning multi-element correlations among basalt compositional datasets, including mid-ocean ridge, ocean island, and island arc basalts. The PRMs were designed to estimate trace-element compositions from inputs of 2–9 selected trace elements, and basalt trace-element compositions (e.g., Rb, Ba, U, K, Pb, Sr, and rare earth elements) were estimated from only four inputs with a reproducibility of ~0.1 log10 units (i.e., ±25%). Using Th, Nb, Zr, and Ti, which are typically immobile during metamorphism, as input elements, the PRM was verified by applying it to seafloor altered basalt with known protoliths. When suitable immobile elements are incorporated, a PRM can yield unbiased and accurate mass transfer analysis of any metabasalt with unknown protolith.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
Loading...