Fuzzy Modeling for Reserve Estimation Based on Spatial Variability

Tutmez, Bulent; Tercan, Adnan; Kaymak, Uzay

doi:10.1007/s11004-006-9066-4

Cited by 28 publications

(20 citation statements)

References 19 publications

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“…The range values taken these experimental structures are used to define the width (spread) of the hidden units. In practice, Gaussian membership functions are more common in geosciences, as they provide both simplicity and flexibility [13]. Determination of the width (r) of the Gaussian-type radial basis function is indicated in In the last stage, the unknown model parameters stated in Sect.…”

Section: Methodsmentioning

confidence: 99%

“…Semimadogram is a spatial measure function, and it is particularly useful for establishing range parameter [12]. The point cumulative semimadogram (PCSM), which is a special form of semimadogram, has been proposed by [13] and applied to different problems. PCSM can be used in determining the spatial behaviour of any variable around a particular cluster.…”

Section: Measuring Spatial Correlationmentioning

confidence: 99%

“…Starting from this, the PCSM function is suggested for defining the width r. In practice, r value is a constant that determines the spread of the function [13] and it can be determined from the range values of the modelled PCSMs.…”

Section: Measuring Spatial Correlationmentioning

confidence: 99%

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Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Tutmez

2010

Neural Comput & Applic

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Aquifer porosity indicates the storage groundwater capacity and groundwater quality. It may be measured via different techniques. This paper presents a novel spatial methodology based on radial basis function (RBF) and neuro-fuzzy inference system for modelling the porosity. Use of the point cumulative semimadogram in RBF as a spatial measure is a novel contribution. In addition, the methodology examines the use of a neural network-based fuzzy inference system for porosity estimation. Performance comparisons with conventional methods show that the proposed spatial model has high modelling and generalization capability.

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Section: Methodsmentioning

confidence: 99%

Section: Measuring Spatial Correlationmentioning

confidence: 99%

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Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Tutmez

2010

Neural Comput & Applic

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“…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). Fuzzy modeling for grade and reserve estimation is a very effective method for mining cost assessments (Pham 1997;Bardossy & Fodor 2001;Tütmez et al 2007). Integrating geostatistical concepts with fuzzy set theory (Bardossy et al 1990) is a novel direction, and the application of fuzzy modeling in reserve estimation is very limited.…”

Section: Introductionmentioning

confidence: 99%

Computation of grade values of sediment-hosted barite deposits in northeastern Isparta (western Turkey)

ELMAS¹,

Şahin²

2013

Turkish J Earth Sci

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“…Recently, Tutmez, Tercan and Kaymak (2007) have carried out a study that tries to combine fuzzy algorithms and spatial variability in reserve estimation. Fuzzy logic has been successfully applied as a quality evaluation tool for other resources-such as kaolin (Taboada et al, 2006b) and slate (Taboada et al, 2006a)-in which the boundaries between different qualities (defined using a set of physical, chemical and aesthetic parameters) are not clearly defined.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the reserve of a granite deposit by fuzzy kriging

Taboada

Rivas

Saavedra

et al. 2008

Engineering Geology

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In the present study, we describe a method for estimating the reserve in an ornamental granite deposit. The method is adapted to the existing optimised mining method and provides a more realistic estimate of commercial quality than conventional methods. Using a batholith geology-mining map, a description of fracturing in the mining area, and underground information provided by continuous-core boreholes, an estimate of reserves was made that featured the following innovations: 1) the use of fuzzy set theory to assign qualities to each block, and 2) the inclusion of fracture anisotropy at the deposit level in the fuzzy mathematical formulation. These innovations enabled quality distributions in the rock mass to be assessed more realistic, by reducing the estimation error caused by assigning a single quality to the primary block and by improving the estimate of the distribution of commercial qualities defined by fracturing. This method produced a satisfactory estimate of the commercial qualities of one of the world's most important granite deposits (in 2 northwest Spain). The results have been stored in a database-linked to a graphic representation of the blocks (similar to a geographical information system)-which, by providing automatic access to information in regard to the location of the best quality commercial blocks, ultimately optimises mining planning.

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Fuzzy Modeling for Reserve Estimation Based on Spatial Variability

Cited by 28 publications

References 19 publications

Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Computation of grade values of sediment-hosted barite deposits in northeastern Isparta (western Turkey)

Evaluation of the reserve of a granite deposit by fuzzy kriging

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