MétaCan
Menu
← Back to cohort
Record W2599283420 · doi:10.22067/econg.v8i2.60499

Geology, Geochemistry and Ground Magnetic Survey on Kalateh Naser Iron Ore Deposit, Khorasan Jonoubi Province

2017· article· en· W2599283420 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeochemistryGeologyMagnetic surveyMining engineeringIron oreArchaeologyMagnetic anomalyGeographyGeophysics

Abstract

fetched live from OpenAlex

Introduction Ground magnetometer surveys is one of the oldest geophysical exploration methods used in identifying iron reserves. The correct interpretation of ground magnetic surveys, along with geological and geochemical data will not only reduce costs but also to indicate the location, depth and dimensions of the hidden reserves of iron (Robinson and Coruh, 2005; Calagari, 1992). Kalateh Naser prospecting area is located at 33° 19َ to 33° 19ََ 42" latitude and 60° 0' to 60° 9َ 35" longitude in the western side of the central Ahangaran mountain range, eastern Iran (Fig.1). Based on primary field evidences, limited outcrops of magnetite mineralization were observed and upon conducting ground magnetic survey, evidence for large Iron ore deposits were detected (Saadat, 2014). This paper presents the geological and geochemical studies and the results of magnetic measurements in the area of interest and its applicability in exploration of other potential Iron deposits in the neighboring areas. Materials and methods To better understand the geological units of the area, samples were taken and thin sections were studied. Geochemical studies were conducted through XRF and ICP-Ms and wet chemistry analysis. The ground magnetic survey was designed to take measurements from grids of 20 meter apart lines and 10 meter apart points along the north-south trend. 2000 points were measured during a 6-day field work by expert geophysicists. Records were made by Canadian manufactured product Magnetometer Proton GSM19T (Fig. 2). Properties of Proton Magnetometer using in magnetic survey in Kalateh Naser prospecting area is shown in Table 1. Total magnetic intensity map, reduced to pole magnetic map, analytic single map, first vertical derivative map and upward continuation map have been prepared for this area. Results The most significant rock units in the area are cretaceous carbonate rocks (Fig. 3). The unit turns to shale and thin bedded limestone in the central part and into red and white crystalline limestone towards the west, which sometimes can be referred as marble and skarn (Figs. 4, 5 and 6; Saadat, 2014). Iron mineralization is mostly observed in these units. Acidic to intermediate intrusive bodies consisting hornblende quartz monzonite, biotite granodiorite, pyroxene quartz diorite have outcrops in the north and northwestern part of the area (Fig. 3). Outcrops from andesite to dacite volcanic rocks in combination with ultra-mafic rocks can be seen in the southern part of the region. The geochemical results indicated F2O3 value range of %31 to %96. P2O5 of maximum %0.45 was observed and TiO2 varied from %0.02 to %0.54 (Tables 2 and 3 and Figs 7, 8 and 9). The highest values of iron and copper are found in the northern part, titanium and phosphorus are located in the southern part and manganese and vanadium are placed in the central sector. According to the obtained results, the highest magnetic susceptibility was associated with the skarn units and was measured at 34000*10-5 SI which is related to the mineralization of Iron in the area. Magnetic susceptibility of limestone crystalline units were close to 50*10-5 SI and marble was less than 10* 10-5 SI which highlights the influence of iron mineralization in the carbonates rocks. This value was around 80*10-5 SI for intrusive rocks such as hornblende quartz monzonite in the area (Table 4). Ground magnetic studies suggest minimum of 40000 nT and maximum of 70000 nT total magnetic intensity in the area (Fig. 10-A; Ryahei, 2013). Utilizing the Reduced to Pole Magnetic Filter is to locate the anomalies in the study area (Fig. 11-A). Since magnetic declination causes a degree of deviation between the source and magnetic anomalies, the said filter is applied to magnetic data and ultimately, analysis is done based on the magnetic data transferred to the pole (Nakatsuka and Okuma, 2006; Clark, 1997). The results of reduce to pole magnetic map for this area yielded three large and two small magnetic anomalies (Figs 12-A and 12-B). The upward continuation maps were taken with 5m, 10m, 20m, 30m, 40m, and 50m. Smaller anomalies tend to disappear more comparing the 5m to 20m continuation maps respectively, and a homogenous large anomaly starts to form in the 50m map (Fig. 13). Large and clear anomalies continue to be present in the 50m continuation map and only two smaller anomalies are disappeared from the west of the area (Ryahei, 2013). Discussion The results of geological, geochemical and magnetic susceptibility measurements indicate that magnetic anomalies in the Kalate-Naser area is related to the iron mineralization in this area. Lower amount of magnetic susceptibility in intrusive mass outcrops also indicate that these intrusive rocks did not play the main role in iron mineralization and were in fact have been weakly altered. It can only be concluded that the intrusive mass that led to mineralization sits beneath, at a higher depth. The initial geophysical survey results are closely comparable to the powder drilling trials that confirm magnetite mineralization to the named depth (Saadat, 2014). Thus far, 1.5 Million ton of Iron ore deposits have been confirmed in the area and exploration continues during production. The obtained results once again highlight the importance of ground magnetic surveys that combined with other exploration methods can reduce costs, increase efficiency and simplify the exploration process. Methodology and results of the magnetic measurements conducted in Kalateh Naser can help to better understand the magnetite bodies in the neighboring areas. Acknowledgement Hereby, I would like to thank my colleagues particularly, Ryahi, Shokri, Madani, Ebrahimzadeh, Salari, Maldar family, Ghoorchi, amongst others that assisted with field visits, mapping, processing and data analysis. References Calagari, A.A., 1992. Principals of geophysics exploration. Tabesh press, Tabriz, 588 pp. Clark. D.A., 1997, Magnetic petrophysics and magnetic petrology: aids to geological interpretation of magnetic surveys. Journal of Australian Geology and Geophysics, 17(2): 83-103. Nakatsuka, T. and Okuma S., 2006. Reduction of magnetic anomaly observations from helicopter surveys at varying elevations. Exploration Geophysics, 37(1): 121-128. Robinson, E.S. and Coruh, C. (translated by Haydarian Shahri, M.R.), 2005. Basic exploration geophysics. Ferdowsi University of Mashhad Press, Mashhad, 750 pp. Ryahei, H., 2013. Magnetite data of Kalateh Naser prospecting area. Madan Yaran Lut Company, Mashhad, Internal report, Report 1, 17 pp. Saadat, S., 2014. Final exploration report of Kalateh Naser prospecting area. Madan Yaran Lut Company, Mashhad, Internal report, Report 3, 100 pp.

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)0.000
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.128
GPT teacher head0.427
Teacher spread0.299 · 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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicGeological and Geochemical Analysis→French-language works237,207→