The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj–Sirjan metallogenic zone, Iran

Meshkani, Seyed Ahmad; Mehrabi, Behzad; Yaghubpur, Abdolmajid; Alghalandis, Younes Fadakar

doi:10.1016/j.gexplo.2011.01.006

Cited by 22 publications

(9 citation statements)

References 34 publications

(42 reference statements)

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“…(Ehya et al, 2010;Ghorbani et al, 2000). The most important metallogenic provinces for Pb-Zn mineralization are in Central Iran, the Sanandaj-Sirjan Zone, and the Alborz region (Ghazanfari, 1999;Meshkani et al, 2011), as depicted in Fig. 1.…”

Section: Geological Setting Of the Case Studiesmentioning

confidence: 99%

“…The Pb-Zn deposits are common in the Sanandaj-Sirjan Zone by 1500 km long, up to 200 km wide, and extend from northwest to southeast Iran, especially in its middle part, the Malayer-Esfahan belt, where it is predominantly stratabound and restricted to Cretaceous limestones, dolomites, shales, and occasionally sandstones, although some deposits have pre-Cretaceous host rocks (Meshkani et al, 2011;Momenzadeh, 1976). Sulfidic mineralization and non-sulfide ores are dominant in this belt.…”

Section: Geological Setting Of the Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Delineation of mineralization zones using concentration–volume fractal method in Pb–Zn carbonate hosted deposits

Delavar

Afzal

Borg

et al. 2012

Journal of Geochemical Exploration

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Section: Geological Setting Of the Case Studiesmentioning

confidence: 99%

Section: Geological Setting Of the Case Studiesmentioning

confidence: 99%

Delineation of mineralization zones using concentration–volume fractal method in Pb–Zn carbonate hosted deposits

Delavar

Afzal

Borg

et al. 2012

Journal of Geochemical Exploration

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“…The K-means methods are used to properly analyze the data behavior and available analyses to each other. Some of its applications include: the division of the geological terrain [10], the classification of the effect of vegetation and the recovery of water health in the Mediterranean coast forests [11], the presentation of geochemical patterns in mineral areas [12], predicting the organic carbon in the intelligent systems [13], and determining the effect of gas diffusion in urban environments [14].…”

Section: Introductionmentioning

confidence: 99%

Geochemical and Geostatistical Studies for Estimating Gold Grade in Tarq Prospect Area by K-Means Clustering Method

Shirazy¹,

Ширази²,

Ferdossi³

et al. 2019

OJG

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Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran's Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.

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“…For example, Ji et al (2007) devel-oped semi-hierarchical correspondence cluster analysis and showed its application for division of geological units with the help of geochemical data that are systematically collected from an area around Tahe in Heilongjiang Province, north China. Meshkani et al (2011) used hierarchical and k-means clustering for identifying distribution of lead and zinc in the Sanandaj-Sirjan metallogenic zone in Iran. Ziaii et al (2009) introduced the neuro-fuzzy method for separating anomalies and showed that this method is more efficient than using multivariate statistics.…”

Section: Introductionmentioning

confidence: 99%

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Sarparandeh

Hezarkhani

2017

Geosci. Instrum. Method. Data Syst.

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Abstract. The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of SeChahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) -including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and selforganizing map (SOM) -were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed methods and geological studies leads to finding some hidden information, and this approach has the best results compared to using only one of them.

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The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj–Sirjan metallogenic zone, Iran

Cited by 22 publications

References 34 publications

Delineation of mineralization zones using concentration–volume fractal method in Pb–Zn carbonate hosted deposits

Delineation of mineralization zones using concentration–volume fractal method in Pb–Zn carbonate hosted deposits

Geochemical and Geostatistical Studies for Estimating Gold Grade in Tarq Prospect Area by K-Means Clustering Method

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

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