An uncertainty oriented fuzzy methodology for grade estimation

Tutmez, Bulent

doi:10.1016/j.cageo.2006.09.001

Cited by 28 publications

(18 citation statements)

References 6 publications

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“…In global methods, the interpolation depends on all sample points, while the local interpolation methods depend only on the data in its neighborhood [4]. Additionally, artificial neural networks [14], fuzzy methodology [15,16], evolutionary algorithms [17], support vector machines [18], and fractal models [19] have also been used to estimate the grade of the mineralized deposits.…”

Section: Introductionmentioning

confidence: 99%

Grade Distribution Modeling within the Bauxite Seams of the Wachangping Mine, China, Using a Multi-Step Interpolation Algorithm

Wang

2017

Minerals

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Abstract:Mineral reserve estimation and mining design depend on a precise modeling of the mineralized deposit. A multi-step interpolation algorithm, including 1D biharmonic spline estimator for interpolating floor altitudes, 2D nearest neighbor, linear, natural neighbor, cubic, biharmonic spline, inverse distance weighted, simple kriging, and ordinary kriging interpolations for grade distribution on the two vertical sections at roadways, and 3D linear interpolation for grade distribution between sections, was proposed to build a 3D grade distribution model of the mineralized seam in a longwall mining panel with a U-shaped layout having two roadways at both sides. Compared to field data from exploratory boreholes, this multi-step interpolation using a natural neighbor method shows an optimal stability and a minimal difference between interpolation and field data. Using this method, the 97,576 m 3 of bauxite, in which the mass fraction of Al 2 O 3 (W a ) and the mass ratio of Al 2 O 3 to SiO 2 (W a/s ) are 61.68% and 27.72, respectively, was delimited from the 189,260 m 3 mineralized deposit in the 1102 longwall mining panel in the Wachangping mine, Southwest China. The mean absolute errors, the root mean squared errors and the relative standard deviations of errors between interpolated data and exploratory grade data at six boreholes are 2.544, 2.674, and 32.37% of W a ; and 1.761, 1.974, and 67.37% of W a/s , respectively. The proposed method can be used for characterizing the grade distribution in a mineralized seam between two roadways at both sides of a longwall mining panel.

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Section: Introductionmentioning

confidence: 99%

Grade Distribution Modeling within the Bauxite Seams of the Wachangping Mine, China, Using a Multi-Step Interpolation Algorithm

Wang

2017

Minerals

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“…Mining investment costs can be decreased using feasible grade estimation methods. Grade estimation contains many uncertainties, which may be due to the sampling, the natural characteristics of an ore deposit, and the analytical error of the chemical and mineralogical analyses (Tütmez 2007). This uncertainty factor in grade estimation leads to the need to develop new estimation methodologies by which financiers and managers can be assisted in evaluating their mining projects with a minimum risk of incorrect prediction (Pham 1997).…”

Section: Introductionmentioning

confidence: 99%

“…A number of methods such as geometrical and geostatistical approaches have been developed for the purpose of grade estimation. Geometrical methods (David 1977) depend on geometrical relationships between sample points, while geostatistical methods (Journel & Huijbregts 1981;Goovaerts 1997) are based on random functions and consider spatial relationship of the sample data used in the analysis (Tütmez 2007). The most important shortcoming of the geostatistical methods is the amount of data.…”

Section: Introductionmentioning

confidence: 99%

“…One of these mathematical methods, fuzzy set and fuzzy modeling theory, which provides new tools for describing uncertain systems using rule bases and new techniques for the inference mechanism, has been applied in reserve estimation (Pham 1997;Tütmez 2007;Tütmez & Dağ 2007). Fuzzy set theory plays an important role in dealing with uncertainty when making decisions in applications (Dubois & Prade 1998;Kuncheva et al 1999;Nauck & Kruse 1999).…”

Section: Introductionmentioning

confidence: 99%

“…The main shortcomings of these works were that the spatial variability of data values could not be used in the algorithms. However, the spatial positions of data directly connected with data values (grades) are very important for reserve estimation (Tütmez 2007). Recently, Luo and Dimitrakopoulos (2003), Bardossy et al (2003), Bardossy & Fodor (2005), and Tütmez (2005) have applied the fuzzy set theory in resource estimation and mathematically evaluated the spatial continuity of ore bodies by using fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

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