Development of a GIS-based information system for mining activities: Afsin-Elbistan lignite surface mine case study

Mert, Bayram Ali; Dağ, Ahmet

doi:10.1504/ijogct.2015.067489

Cited by 3 publications

(4 citation statements)

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“…1). In a geostatistical analysis, all these components must be systematically accounted for [29][30][31].…”

Section: Basics Of Geostatistical Estimationsmentioning

confidence: 99%

“…where z is an observed value at a particular location, h is the distance between ordered data, and N(h) is the number of paired data at a distance of h [31]. Before using experimental semivariogram in the kriging estimation process, the most appropriate theoretical model representing the experimental semivariogram needs to be determined.…”

Section: Semivariogram Analysesmentioning

confidence: 99%

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A Computer Program for Practical Semivariogram Modeling and Ordinary Kriging: A Case Study of Porosity Distribution in an Oil Field

Mert

Dağ

2017

Open Geosciences

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Abstract:In this study, firstly, a practical and educational geostatistical program (JeoStat) was developed, and then example analysis of porosity parameter distribution, using oilfield data, was presented. With this program, two or three-dimensional variogram analysis can be performed by using normal, log-normal or indicator transformed data. In these analyses, JeoStat offers seven commonly used theoretical variogram models (Spherical, Gaussian, Exponential, Linear, Generalized Linear, Hole Effect and Paddington Mix) to the users. These theoretical models can be easily and quickly fitted to experimental models using a mouse. JeoStat uses ordinary kriging interpolation technique for computation of point or block estimate, and also uses cross-validation test techniques for validation of the fitted theoretical model. All the results obtained by the analysis as well as all the graphics such as histogram, variogram and kriging estimation maps can be saved to the hard drive, including digitised graphics and maps. As such, the numerical values of any point in the map can be monitored using a mouse and text boxes. This program is available to students, researchers, consultants and corporations of any size free of charge. The JeoStat software package and source codes available at: http://www.jeostat.com/JeoStat_2017.0.rar

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“…1). In a geostatistical analysis, all these components must be systematically accounted for [29][30][31].…”

Section: Basics Of Geostatistical Estimationsmentioning

confidence: 99%

Section: Semivariogram Analysesmentioning

confidence: 99%

A Computer Program for Practical Semivariogram Modeling and Ordinary Kriging: A Case Study of Porosity Distribution in an Oil Field

Mert

Dağ

2017

Open Geosciences

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“…variogram (nokta-blok) olarak alınmıĢtır (ġekil 4), [10]. ġekil 4. a) blok-nokta, b) blok-blok, c) nokta-nokta ortalama variogramların Ģekilsel gösterimi [8] Ağırlık katsayıları toplamının 1'e eĢit çıkması Ģartı altında, kestirim varyansını (EĢitlik 5) minimize edebilmek için Lagrange çarpanları "µ" ele alınmıĢtır.…”

Section: Ordinary Kriging Kestġrġm Teknġğġunclassified

Kriging Tekniği ile Nokta ve Alansal Kestirim: Tenör Dağılımlarının Haritalanması Örneği

Mert¹,

Dağ²,

Özdemir³

2016

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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ÖzGözlemler arasındaki uzaklığa bağlı iliĢki, varyansa dayalı olarak elde edilen variogram fonksiyonlarıyla değerlendirilebilmektedir. Mevcut iliĢkilerin oluĢturduğu ağırlıklardan hareketle kriging gibi çeĢitli jeoistatistiksel araçlar geliĢtirilmiĢtir. Bu çalıĢmada, farklı disiplinlerde çoğunlukla da bilgisayar programları tarafından gerçekleĢtirilen ordinary kriging kestirim tekniği ile noktasal ve alansal tahminlerin hesabı bilgisayar kullanılmadan adım adım ele alınarak yapılmıĢtır. ÇalıĢma kapsamında hipotetik olarak seçilmiĢ bir maden yatağına ait tenör verileri kullanılarak öncelikle örneklenmemiĢ bir noktanın ve alanın tahmini yapılmıĢ, daha sonra maden yatağının tenör dağılımı tahmin edilmiĢ ve tüm tenör verileri kullanılarak haritalanmıĢtır. Temel kriging yaklaĢımının kullanımı, özellikle saha çalıĢmalarına dayalı mühendislik ve fen bilimlerindeki modelleme çalıĢmalarında etkin bir araç olarak kullanımı artıracaktır. AbstractThe semivariogram functions which are obtained as a result of the spatial dependency between samples and the kriging equations used for estimating unsampled point or area are used in many disciplines. Kriging is a complex procedure that requires greater knowledge about spatial statistics and the solutions of kriging equations are usually carried out with the help of computer programs. In this study, point and block estimations of the grade contents in a mineral deposit were made by taking step by step and without using a computer. In the scope of the study, firstly, unsampled point and block grade have estimated using grade samples of the mineral deposit which is hypothetically selected, and then the grade distribution of the mineral deposit have estimated and mapped using all the grade data. The use of the basic kriging approach will enhance its use as an effective tool, especially in modeling studies in engineering and science based on field studies.

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“…In a different study, a software based algorithm suggested for handling well-log data to estimate the proximate parameters of coal beds. As a result of this, a GIS-based information system was suggested (Mert and Dag 2015). In general, investigations and decisions incorporating energy sources are generally characterized via high complexities.…”

Section: Introductionmentioning

confidence: 99%

Learning distance effect on lignite quality variables at global and local scales

Yaylagul

Tutmez

2020

Int J Coal Sci Technol

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Determining scale and variable effects have critical importance in developing an energy resource policy. This study aims to explore the relationships in heterogeneous lignite sites using different scale models, spatial weighting as well as error-based pair-wise identification. From a statistical learning framework, the relationships among the quality variables such as geochemical variables and the contributions of the coordinates to quality measures have been exhibited by generalized additive models. In this way, the critical roles of spatial weights provided by the coordinates have been specified at a global scale. The experimental studies reveal that incorporating the geological weighting in the models as the additional information improves both accuracy and transparency. Because relationships among lignite quality variables and sampling locations are spatially non-stationary, the local structure and interdependencies among the variables were analyzed by geographically weighting regression. The local analyses including spatial patterns of bandwidths, search domains as well as residual-based areal dependencies provided not only the critical zones but also availability of pair-wise model alternatives by calibrating a model at each point for location-specific parameter learning. The results completely show that the weighting models applied at different scales can take spatial heterogeneity into consideration and these abilities provide some meta-data and specific information using in sustainable energy planning.

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Development of a GIS-based information system for mining activities: Afsin-Elbistan lignite surface mine case study

Cited by 3 publications

References 0 publications

A Computer Program for Practical Semivariogram Modeling and Ordinary Kriging: A Case Study of Porosity Distribution in an Oil Field

A Computer Program for Practical Semivariogram Modeling and Ordinary Kriging: A Case Study of Porosity Distribution in an Oil Field

Kriging Tekniği ile Nokta ve Alansal Kestirim: Tenör Dağılımlarının Haritalanması Örneği

Learning distance effect on lignite quality variables at global and local scales

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