Application of geostatistical hierarchical clustering for geochemical population identification in Bondar Hanza copper porphyry deposit

Madani, Nasser; Maleki, Mohammad; Sepidbar, Fatemeh

doi:10.1016/j.chemer.2021.125794

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…In data mining and statistics, "hierarchical clustering" is a method that performs the act of categorizing and grouping observations and data in a hierarchical manner. The point that distinguishes this method from other clustering methods is the sequence and topdown (or bottom-up) view that exists in this technique (Khakmardan et al 2020a;Khayer et al 2021;Madani et al 2021;Nazerian, Catania, et al 2022;. For clustering, the hierarchical distance clustering method was used and the results were presented according to Table 5 and the dendrogram in Figure 15.…”

Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

Shirazi,

Shirazy,

Hosseini

et al. 2022

Ruhuna J. Sci.

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Stream sediment samples play an important role in identifying potential areas of metallic and non-metallic mineralization in mineral exploration studies. The relationship of geochemical elements with each other shows how the elements are distributed in the area. Also, by identifying related elements, sampling and targeted chemical analysis can be used in the next stages of exploration. The purpose of this study is to investigate the elements related to the copper element in the Siojan prospecting area, which is located in South-Khorasan province and 30 km northwest of Birjand city of Iran. In Siojan area, 120 stream sediment samples of a 60 square kilometer area were collected to detect geochemical anomalies and were consequently analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for 45 elements. Preliminary geological studies showed that the studied area has copper mineralization potential, and therefore, copper was selected as the target element in this study. Copper trace elements were identified in the area and the results were used to identify copper mineralized anomalies. For the elemental analysis data, methods of Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and K-Means Clustering were performed to identify the relevant elements and relationships among them. Statistical analysis of the concentration of geochemical elements in the region revealed that copper and cobalt elements were identified as two elements of the same family in terms of geochemical genetics. The average value for copper and cobalt elements in the analyzed samples was 27.2 ppm and 15.5 ppm, respectively. Finally, the relationship between copper and cobalt elements was modeled as an equation using the K-Means Clustering algorithm.

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Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

Shirazi,

Shirazy,

Hosseini

et al. 2022

Ruhuna J. Sci.

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A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

Nasretdinova,

Madani,

Maleki

2024

Nat Resour Res

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The increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

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A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

Hajihosseinlou,

Maghsoudi,

Ghezelbash

2024

Geochemistry

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Application of geostatistical hierarchical clustering for geochemical population identification in Bondar Hanza copper porphyry deposit

Cited by 4 publications

References 51 publications

Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

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