The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit

Badel, Mehdi; Angorani, Saeed; Panahi, Masoud Shariat

doi:10.1016/j.cageo.2010.07.009

Cited by 50 publications

(16 citation statements)

References 5 publications

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“…Based on the frequency diagram of the T 3 tetrad effects for all samples of the studied district, it is clear that the distribution pattern of the computed T 3 tetrad effect values present a substantial bimodal pattern (Fig. 8a), which can be due to various geochemical conditions and/or the geological environment during deposition (Badel et al, 2011). The clusters of the samples in this district were classified on the basis of the geological relationships, spatial distribution and field observations ( Fig.…”

Section: Occurrence Of the Tetrad Effect In Kaolin Samplesmentioning

confidence: 98%

The Hizeh‐Jan Kaolin Deposit of NW Iran: the Tetrad Effect in REE Distribution Patterns

Abedini

Azizi

2019

Acta Geologica Sinica (Eng)

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The Hizeh‐Jan kaolin deposit (northwest of Varzeghan, East‐Azarbaidjan Province, NW Iran) is a product of the alteration of Eocene andesitic rocks. Based on mineralogical examinations, kaolinite, quartz, smectite, pyrophyllite, muscovite‐illite, alunite, calcite, diaspore, goethite and hematite are the most abundant mineral phases in this deposit. The geochemical indicators, such as Y/Ho and Zr/Hf, indicate the non‐CHARAC (non‐Charge‐radius control) behavior of these pairs, which are likely to be due to the occurrence of the tetrad effect phenomenon in this deposit. Simultaneous concave and convex shapes in the chondrite‐normalized REE distribution patterns are a remarkable feature of the kaolin samples. Bivariate diagrams of the size of the third tetrad effect (T3) versus geochemical parameters such as Y/Ho, Nb/Ta and Zr/Hf ratios display two distinct populations for the kaolin samples. The first population is characterized by high T3 values (>0.13), which are near or on the fault zone. The second population is characterized by low T3 values (<0.13), and are farther from the fault zone. The obtained results from the geochemical data have furnished compelling evidence that fluid‐rock interaction, overprint of hypogene processes by supergene ones, and structural control, are key controlling factors for the occurrence of tetrad effects in REE distribution patterns in the Hizeh‐Jan kaolin deposit.

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Section: Occurrence Of the Tetrad Effect In Kaolin Samplesmentioning

confidence: 98%

The Hizeh‐Jan Kaolin Deposit of NW Iran: the Tetrad Effect in REE Distribution Patterns

Abedini

Azizi

2019

Acta Geologica Sinica (Eng)

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“…The Kahang ore is divided into three 8. The selective block size is determined based on recoverable resource estimation, mine planning condition, dispersion of ore all over the deposit, borehole data spacing, and considering volume-variance effect (Badel et al, 2011;Hekmat et al, 2013;Rossi and Deutsch, 2014). To estimate the 3D spatial distribution of Cu, the ordinary block kriging is performed with block size 20 m × 20 m × 10 m.…”

Section: Designing the Pattern Of Boreholes For Real Datamentioning

confidence: 99%

A novel algorithm for designing the layout of additional boreholes

Morshedy

Memarian

2015

Ore Geology Reviews

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“…Indicator kriging converts the grade of the sample to the indicators of a 0 or 1 based on the cut-off grade prior to the variogram modeling. This method does not predict the grade of the block, but evaluates the possibility that the grade of the block is higher than the selected cut-off grade [22,23]. The purpose of this study is to evaluate the applicability of the proposed hot spot top-cut method for grade estimation.…”

Section: Variography and Grade Estimationmentioning

confidence: 99%

New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data

2018

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Three-dimensional (3D) analysis of borehole data is very important for effective mineral exploration. It can be used not only to understand the geological structure of the underground, but to estimate the amount of the resource. In the mining industry, the geostatistical interpolation, such as kriging, is widely used to predict the value of a whole section using this borehole data. In order to obtain reasonable prediction results, it is firstly necessary to verify assay and geological databases. In addition, if the assayed grade data deviates significantly from the average value, it is necessary to perform the prediction including the outlier top-cut because it may excessively affect the predicted value. However, the existing top-cut methods of determining a specific threshold value may cause an error by excluding significant data. In this study, to minimize the loss of such data, we developed a 3D hot spot analysis technique to analyze statistically significant outliers. In addition, it was applied to borehole data analysis of the Au deposit. As a result, we confirmed that the proposed method can mitigate the overestimation or underestimation that might occur when applying the existing methods.

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The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit

Cited by 50 publications

References 5 publications

The Hizeh‐Jan Kaolin Deposit of NW Iran: the Tetrad Effect in REE Distribution Patterns

The Hizeh‐Jan Kaolin Deposit of NW Iran: the Tetrad Effect in REE Distribution Patterns

A novel algorithm for designing the layout of additional boreholes

New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data

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