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Record W4311911431 · doi:10.5382/econgeo.4967

Geochemical Signatures of Felsic Volcanic Rocks in Modern Oceanic Settings and Implications for Archean Greenstone Belts

2022· article· en· W4311911431 on OpenAlexafffund
Marc Lorin Fassbender, Mark D. Hannington, Margaret S. Stewart, Philipp A. Brandl, Alan T. Baxter, David Diekrup

Bibliographic record

VenueEconomic Geology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMount Royal UniversityUniversity of Ottawa
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaGEOMAR Helmholtz-Zentrum für Ozeanforschung KielMaterials and Energy Research CenterBundesministerium für Bildung und Forschung
KeywordsFelsicGeologyVolcanic rockArcheanGeochemistryGreenstone beltFractional crystallization (geology)RhyoliteBasaltZirconAdakiteOceanic crustPetrologyMaficEarth scienceVolcanoSubductionPaleontologyTectonics

Abstract

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Abstract Felsic volcanic rocks are abundant in ancient greenstone belts and important host rocks for volcanogenic massive sulfide (VMS) deposits. About half of all VMS deposits are hosted by dacite or rhyolite, an association that reflects anomalous heat flow during rifting, partial melting of basaltic crust, and fractional crystallization in high-level magma chambers. For over 30 years, geochemical signatures of these rocks (e.g., F classification of Archean rhyolites) have been widely used to identify possible hosts for VMS deposits in ancient greenstone belts. However, comparisons with modern oceanic settings have been limited, owing to a lack of samples of felsic volcanic rocks from the sea floor. This is changing with increasing exploration of the oceans. In this study, we have compiled high-quality geochemical analyses of more than 2,200 unique samples of submarine felsic volcanic rocks (>60 wt % SiO2) from a wide range of settings, including mid-ocean ridges, ridge-hot-spot intersections, intraoceanic arc and back-arc spreading centers, and ocean islands. The compiled data show significant geochemical diversity spanning the full range of compositions of rhyolites found in ancient greenstone belts. This diversity is interpreted to reflect variations in crustal thickness, the presence or absence of slab-derived fluids (dry melting versus wet melting), and mantle anomalies. Highly variable melting conditions are thought to be related to short-lived microplate domains, such as those caused by diffuse spreading and multiple overlapping spreading centers. Systematic differences in the compositions of felsic volcanic rocks in the modern oceanic settings are revealed by a combination of principal components analysis, unsupervised hierarchical clustering, and supervised random forest classification of the compiled data. Dacites and rhyolites from midocean ridge settings have moderately depleted mantle signatures, whereas rocks from ridge-hot-spot intersections and ocean islands reflect enriched mantle sources. Felsic volcanic rocks from arc-back-arc systems have strongly depleted mantle signatures and well-known subduction-related chemistry (strong large ion lithophile element enrichment in combination with strong negative Nb-Ta anomalies and low heavy rare earth elements [HREEs]). This contrasts with felsic volcanic rocks in Archean greenstone belts, which show high field strength element and HREE enrichment (so-called FIIIb-type) due to a less depleted mantle, a lack of wet melting, and variable crustal contamination. The differences between modern and ancient volcanic rocks are interpreted to reflect the lower mantle temperatures, thinner crust, and subduction-related processes in present-day settings. We suggest that the abundance of FIIIb-type felsic volcanic rocks in Archean greenstone belts is related to buoyant microplate domains with thickened oceanic crust that were better preserved on emerging Archean cratons, whereas in post-Archean tectonic settings most of these rocks are subducted.

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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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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.

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".

Quick stats

Citations8
Published2022
Admission routes2
Has abstractyes

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