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Hadean tectonics: Insights from machine learning.

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Title: Hadean tectonics: Insights from machine learning.
Authors: Guoxiong Chen1, Kusky, Timothy1,2, Lei Luo1, Quanke Li1, Qiuming Cheng1,3 qiuming.cheng@iugs.org
Source: Geology. Aug2023, Vol. 51 Issue 8, p718-722. 5p.
Subject Terms: *Rare earth metals, *Hadean, *Machine learning, *Phanerozoic Eon, *Rifts (Geology), *Zircon
Geographic Terms: Australia
Abstract: The tectonic affiliations and magma compositions that formed Earth’s earliest crusts remain hotly debated. Previous efforts toward this goal have relied heavily on determining the provenance of Hadean zircons using low-dimensional discriminant diagrams developed from Phanerozoic samples, which are inadequate for capturing systematic differences without considering secular changes in zircon composition. Here, we developed high-dimensional machine learning (ML) approaches using zircon chemistry data (spanning 19 elements over 4.0 b.y.) to characterize zircons that crystallized in some typical tectonic settings (e.g., arcs, plume-related hotspots, and rifts) and from either igneous (I-type) or sedimentary (S-type) magmas. The proposed ML method, from a nonuniformitarian perspective, identifies the tectonic settings and granitoid types of given zircons (from Archean to Phanerozoic) at a higher prediction accuracy of >89% compared to ∼66%–82% for traditional discriminant diagrams (e.g., U/Yb vs. Y and rare earth elements (REE) + Y vs. P). The ML-based discriminators depend on the systematic differences in zircon chemistry, notably, significant differences in U, Th, and heavy REE for tectonic settings, and P and Hf for I- and S-type magmas. Application of the trained ML models to Hadean zircons from Jack Hills, Australia, suggests that these zircons were mainly crystallized in continental arc–forming magmas (90%) with 45% belonging to S-type melts. This result provides clear evidence of sediment recycling associated with subduction activity in the Hadean. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 166919643
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Geology%22">Geology</searchLink>. Aug2023, Vol. 51 Issue 8, p718-722. 5p.
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  Data: *<searchLink fieldCode="DE" term="%22Rare+earth+metals%22">Rare earth metals</searchLink><br />*<searchLink fieldCode="DE" term="%22Hadean%22">Hadean</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Phanerozoic+Eon%22">Phanerozoic Eon</searchLink><br />*<searchLink fieldCode="DE" term="%22Rifts+%28Geology%29%22">Rifts (Geology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Zircon%22">Zircon</searchLink>
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  Label: Abstract
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  Data: The tectonic affiliations and magma compositions that formed Earth’s earliest crusts remain hotly debated. Previous efforts toward this goal have relied heavily on determining the provenance of Hadean zircons using low-dimensional discriminant diagrams developed from Phanerozoic samples, which are inadequate for capturing systematic differences without considering secular changes in zircon composition. Here, we developed high-dimensional machine learning (ML) approaches using zircon chemistry data (spanning 19 elements over 4.0 b.y.) to characterize zircons that crystallized in some typical tectonic settings (e.g., arcs, plume-related hotspots, and rifts) and from either igneous (I-type) or sedimentary (S-type) magmas. The proposed ML method, from a nonuniformitarian perspective, identifies the tectonic settings and granitoid types of given zircons (from Archean to Phanerozoic) at a higher prediction accuracy of >89% compared to ∼66%–82% for traditional discriminant diagrams (e.g., U/Yb vs. Y and rare earth elements (REE) + Y vs. P). The ML-based discriminators depend on the systematic differences in zircon chemistry, notably, significant differences in U, Th, and heavy REE for tectonic settings, and P and Hf for I- and S-type magmas. Application of the trained ML models to Hadean zircons from Jack Hills, Australia, suggests that these zircons were mainly crystallized in continental arc–forming magmas (90%) with 45% belonging to S-type melts. This result provides clear evidence of sediment recycling associated with subduction activity in the Hadean. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=166919643
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      – Type: doi
        Value: 10.1130/G51095.1
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      – Code: eng
        Text: English
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        PageCount: 5
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      – SubjectFull: Rare earth metals
        Type: general
      – SubjectFull: Hadean
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Phanerozoic Eon
        Type: general
      – SubjectFull: Rifts (Geology)
        Type: general
      – SubjectFull: Zircon
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      – SubjectFull: Australia
        Type: general
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      – TitleFull: Hadean tectonics: Insights from machine learning.
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            NameFull: Guoxiong Chen
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            NameFull: Kusky, Timothy
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            NameFull: Lei Luo
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            NameFull: Quanke Li
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            NameFull: Qiuming Cheng
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            – D: 01
              M: 08
              Text: Aug2023
              Type: published
              Y: 2023
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