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Record W3180058363 · doi:10.1093/petrology/egab037

The Effects of Source Mixing and Fractional Crystallization on the Composition of Eocene Granites in the Himalayan Orogen

2021· article· en· W3180058363 on OpenAlexaff
Peng Gao, Yong‐Fei Zheng, Chris Yakymchuk, Zi‐Fu Zhao, Zi‐Yue Meng

Bibliographic record

VenueJournal of Petrology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAnatexisGeologyFractional crystallization (geology)PorphyriticTourmalineGeochemistryIgneous differentiationMagmaGroup (periodic table)CrystallizationComposition (language)AdakiteIncompatible elementPartial meltingMineralogyIgneous rockCrustSubductionBasaltOceanic crustThermodynamicsChemistryVolcano

Abstract

fetched live from OpenAlex

Abstract Granites are generally the final products of crustal anatexis. The composition of the initial melts may be changed by fractional crystallization during magma evolution. Thus, it is crucial to retrieve the temperature and pressure conditions of crustal anatexis on the basis of the composition of the initial melts rather than the evolved melts. Here we use a suite of ∼46–41 Ma granites from the Himalayan orogen to address this issue. These rocks can be divided into two groups in terms of their petrological and geochemical features. One group has high maficity (MgO + FeOt = 2–4 wt%) and mainly consists of two-mica granites, and is characterized by apparent adakite geochemical signatures, including high Sr concentrations and Sr/Y and La/Yb ratios, and low concentrations of heavy rare earth elements and Y. The other group has low maficity (MgO + FeOt <1 wt%) and consists of subvolcanic porphyritic granites and garnet/tourmaline-bearing leucogranites. This group does not possess apparent adakite signatures. The low-maficity group (LMG) has lower MgO + FeOt contents and the high-maficity group (HMG) has higher Mg# compared with initial anatectic melts determined by experimental petrology and melt inclusions studies. Petrological observations indicate that the HMG and the LMG can be explained as a crystal-rich cumulate and its fractionated melt, respectively, such that the initial anatectic melt is best represented by an intermediate composition. Such a cogenetic relationship is supported by the comparable Sr–Nd isotopic compositions of the two coeval groups. However, these compositions are also highly variable, pointing to a mixed source that was composed of amphibolite and metapelite with contrasting isotope compositions. We model the major and trace element compositions of anatectic melts generated by partial melting of a mixed source at four apparent thermobarometric ratios of 600, 800, 1000 and 1200 °C GPa–1. Modeling results indicate that melt produced at 1000 °C GPa–1 best matches the major and trace element compositions of the inferred initial melt compositions. In particular, a binary mixture generated from 10 vol% partial melting of amphibolite and 30 vol% melting of metapelite at 850 ± 50 °C and 8·5 ± 0·5 kbar gives the best match. Therefore, this study highlights that high thermobarometric ratios and subsequent fractional crystallization are responsible for the generation of the apparent adakitic geochemical signatures, rather than melting at the base of the thickened crust as previously proposed. The thermal anomaly responsible for the Eocene magmatism in the Himalayan orogen was probably related to asthenosphere upwelling in response to rollback of the subducting Neo-Tethyan oceanic slab at the terminal stage of continental collision between India and Asia. As such, a transition in dynamic regime from compression to extension is necessary for the generation of high thermobarometric ratios in continental collision zone. Therefore, after correcting for potential effects of fractional crystallization and crystal accumulation on melt composition, granite geochemistry coupled with thermodynamic modeling can better elucidate the petrogenesis of granites and the geodynamic mechanisms associated with anatexis at convergent plate boundaries.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.188
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations34
Published2021
Admission routes1
Has abstractyes

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