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Important and influential factors in mineralization and grade changes of Manto-copper deposits with a special look to Nasim, Mes e Sorkh and Zarmehr mines

2024· article· en· W6944783458 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsChalcociteMineralization (soil science)MalachiteCopperSedimentary rockMineral resource classificationPolymetallic replacement depositVolcanoBornite

Abstract

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Manto-type copper mines occur in the Bardaskan-Doruneh copper belt. On the basis of structural sedimentary division of Iran, this belt is located in the northeastern part of the Central Iran. At the southern border of this belt, the Yazd structural block and in the north of it is the Sabzevar subzone. The important regional structures of the region are Doruneh and Taknar faults. The majority of the units in this belt are Tertiary volcanic rocks that is associated with some sedimentary units, also some intrusive units are sometimes visible in the form of dyke and stock structures. The main host-rock of mineralization is conglomerate, which is different in grain size, clasts and cement. The most common sulphide mineral is chalcocite and the most important copper carbonates are malachite and azurite. The intense alteration is not observed in the region, and the chloritic alteration is partial, and the alteration of Fe-Oxide, zeolite, calcite, and silica is just observed locally. Mineral solution is reduced and poor in iron and silica. Introduction Manto type copper deposits in the Bardaskan-Doruneh copper belt, with a length of approximately 60 km in a northeast-southwest direction, host more than ten active copper mines such as Zangalu, Zarmehr, Dahane-Siah, Mehr-Ajin, Mes-e-Sorh, Cheshme Hadi, Nasim, Sepidsarw, Cheshme Marzieh and a number of mineral areas, which according to the characteristics of these types of deposits (location, genetic pattern, geometry, host rock, minerals, etc.) and their similarities with Manto type copper deposits in Chile, Canada and the northern Michigan area, it is called as Manto-copper deposits in Iran. Materials and Methods This research includes two parts of field and laboratory investigations. Field studies were carried out in a region with about 60 km in length. Field operations include surveying and searching the area, sampling and distinguishing rock units and mineralization indicators, as well as important geological features of the area. The number of 20 thin sections for lithological investigations, as well as 20 polished thin sections and 10 blocks of drill cores (examination and study of 20,000 meters of drill core related to Nasim, Zarmehr, Mes-e- Sorkh, Cheshme Hadi and Kimia mines) and surface outcrops for a map of mineralization in the area was prepared and studied. Field surveys were conducted for mineralization controllers and host rock changes in the mining pits of Nasim, Zarmehr, Mes-e-Sorkh and Cheshme Hadi mines. Also, due to the importance of tectonic structures, a structural map of the mines was prepared. Discussion and Results The rock units from old to young include: Basalt, Andesite-Basalt, Andesite, Conglomerate with more volcanic fragments, Limestone, Siltstone, Marl and evaporite sediments including gypsum Marl. The general extension of this lithological sequence is northeast-southwest. Mineralization has occurred within the conglomerate unit. There are different types of conglomerate based on the size of the clasts, the lithology of the clasts, and the cement. The type of conglomerate has a great effect on mineralization and grade changes. Conglomerate is a suitable host for mineralization due to the porosity of the rock. The dip and strike of the ore body follows the dip and strike of the lithological units of the region and the deposit is of the stratibound type. Mineralization zone has a variable thickness between 2 and 6 meters (in some places, the thickness increases due to the action of faults). The dip of the ore body varies in different parts and is between 15 and 80 degrees towards the south and southeast. The mineralization texture is mostly disseminated and veined and is mainly as chalcocite mineral. In some places, due to effect of tectonic forces and the formation of fault structures, the order and sequence of units has been dislocated and messed up. Fault structures: The first group of fault structures with the northeast-east to southwest-west direction are the most important fault structures. These fault structures form the boundary between the Andesite unit and the sedimentary units and in the surface of mineralization outcrop can be seen along its length and it is one of the old structures in the region because it has been cut and dislocated many times by other fault structures. The second group is the north-south oriented structures and caused displacements in the mineralized host unit. This fault system, which itself consists of several parallel fault structures, has caused the creation of secondary spaces and the intensification of mineralization, and as a result, the grade has increased in some parts of the region by joining these parallel faults together and with deepening of the basin the intensity of mineralization has increased. The third group of fault structures with the northwest-southeast direction are right lateral strike-slip and minor left lateral strike-slip faults. These structures are one of the most important fault structures in the region, which displaced the rock units of the region, especially the host of mineralization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.447
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), 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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Published2024
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