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Record W3174550521 · doi:10.3390/min11070671

The Relation between Trace Element Composition of Cu-(Fe) Sulfides and Hydrothermal Alteration in a Porphyry Copper Deposit: Insights from the Chuquicamata Underground Mine, Chile

2021· article· en· W3174550521 on OpenAlexaff
Constanza Rivas-Romero, Martín Reich, Fernando Barra, Daniel D. Gregory, Sergio Pichott

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBorniteChalcopyritePyriteGeochemistryGeologyTrace elementPorphyry copper depositHydrothermal circulationSulfideElectron microprobeMineralogySericiteMineralization (soil science)Sulfide mineralsCopperFluid inclusionsChemistry

Abstract

fetched live from OpenAlex

Porphyry Cu-Mo deposits are among the world’s largest source of Cu, Mo, and Re, and are also an important source of other trace elements, such as Au and Ag. Despite the fact that chalcopyrite, bornite, and pyrite are the most common sulfides in this deposit type, their trace element content remains poorly constrained. In particular, little is known about minor and trace elements partitioning into Cu-(Fe) sulfides as a function of temperature and pH of the hydrothermal fluid. In this study, we report a comprehensive geochemical database of chalcopyrite, bornite, and pyrite in the super-giant Chuquicamata porphyry Cu-Mo deposit in northern Chile. The aim of our study, focused on the new Chuquicamata Underground mine, was to evaluate the trace element composition of each sulfide from the different hydrothermal alteration assemblages in the deposit. Our approach combines the electron microprobe analysis (EMPA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of sulfide minerals obtained from six representative drill cores that crosscut the chloritic (propylitic), background potassic, intense potassic, and quartz-sericite (phyllic) alteration zones. Microanalytical results show that chalcopyrite, bornite, and pyrite contain several trace elements, and the concentration varies significantly between hydrothermal alteration assemblages. Chalcopyrite, for example, is a host of Se (≤22,000 ppm), Pb (≤83.00 ppm), Sn (≤68.20 ppm), Ag (≤45.1 ppm), Bi (≤25.9 ppm), and In (≤22.8 ppm). Higher concentrations of Se, In, Pb, and Sn in chalcopyrite are related to the high temperature background potassic alteration, whereas lower concentrations of these elements are associated with the lower temperature alteration types: quartz-sericite and chloritic. Bornite, on the other hand, is only observed in the intense and background potassic alteration zones and is a significant host of Ag (≤752 ppm) and Bi (≤2960 ppm). Higher concentrations of Ag and Sn in bornite are associated with the intense potassic alteration, whereas lower concentrations of those two elements are observed in the background potassic alteration. Among all of the sulfide minerals analyzed, pyrite is the most significant host of trace elements, with significant concentrations of Co (≤1530 ppm), Ni (≤960 ppm), Cu (≤9700 ppm), and Ag (≤450 ppm). Co, Ni, Ag, and Cu concentration in pyrite vary with alteration: higher Ag and Cu concentrations are related to the high temperature background potassic alteration. The highest Co contents are associated with lower temperature alteration types (e.g., chloritic). These data indicate that the trace element concentration of chalcopyrite, bornite, and pyrite changed as a function of hydrothermal alteration is controlled by several factors, including temperature, pH, fO2, fS2, and the presence of co-crystallizing phases. Overall, our results provide new information on how trace element partitioning into sulfides relates to the main hydrothermal and mineralization events controlling the elemental budget at Chuquicamata. In particular, our data show that elemental ratios in chalcopyrite (e.g., Se/In) and, most importantly, pyrite (e.g., Ag/Co and Co/Cu) bear the potential for vectoring towards porphyry mineralization and higher Cu resources.

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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 score0.072
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)0.0000.000

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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

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