Öz
The 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.
Anahtar Kelimeler
Geostatistics Kriging Variogram Spatial analysis Grade distribution
Kaynakça
- 1. Krige, D.G., 1951. A Statistical Approach to Some Basic Mine Valuation Problems on The Witwatersrand, J. of the Chem., Metal. and Mining Soc. of South Africa, vol. 52, p.119-139.
- 2. Matheron, G., 1963. Principles of Geostatistics, Economic Geology, vol. 58, no. 8, p.1246-1266.
- 3. Oliver, M.A., Webster, R., 2014. A Tutorial Guide to Geostatistics: Computing and Modelling Variograms and Kriging. Catena, 113, p. 56-69.
- 4. Webster, R., Oliver, M.A., 2007. Geostatistics for Environmental Scientists. John Wiley & Sons.
- 5. Clark, I., Harper, W.V., 2000. Practical Geostatistics 2000; Ecosse North America Lie. Columbus Ohio, USA, p.342.
- 6. Cressie, N., 1993. Statistics for Spatial Data, Revised Edition; John Wiley & Sons Press, New York, USA, p. 928.
- 7. Wackernagel, H., 2010. Multivariate Geostatistics, 3nd Edition; Springer-Verlag, Deutch, p. 387.
- 8. Srivastava, R.M., 2013. Geostatistics: A Toolkit for Data Analysis, Spatial Prediction and Risk Management in the Coal Industry, International Journal of Coal Geology, vol.112, p.2–13.
- 9. Dag, A., Mert, B.A., 2008. Evaluating Thickness of Bauxite Deposit Using Indicator Geostatistics and Fuzzy Estimation, Resource Geology, 58(2), p. 188-195.
- 10. Mert, B.A., Dag, A., 2015. Development of a GIS-based Information System for Mining Activities: Afsin-Elbistan Lignite Surface Mine Case Study, International Journal of Oil, Gas and Coal Technology 23, 9(2), p. 192-214.
- 11.Chiles, J. P., Delfiner, P., 2009. Geostatistics: Modeling Spatial Uncertainty, vol. 497. John Wiley & Sons.
- 12. Deutch, C.V., Journal, A.G., 1997. GSLIB: Geostatistical Software Library and User’s Guide, 2nd Edition; Oxford University Press, New York, USA, p.369.
- 13.Cressie, N., 1988. Spatial Prediction and Ordinary Kriging, Mathematical Geology, 20(4), p.405-421.
Öz
Gözlemler 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.
Anahtar Kelimeler
Jeoistatistik Kriging Variogram Uzaysal analiz Tenör Dağılımı
Kaynakça
- 1. Krige, D.G., 1951. A Statistical Approach to Some Basic Mine Valuation Problems on The Witwatersrand, J. of the Chem., Metal. and Mining Soc. of South Africa, vol. 52, p.119-139.
- 2. Matheron, G., 1963. Principles of Geostatistics, Economic Geology, vol. 58, no. 8, p.1246-1266.
- 3. Oliver, M.A., Webster, R., 2014. A Tutorial Guide to Geostatistics: Computing and Modelling Variograms and Kriging. Catena, 113, p. 56-69.
- 4. Webster, R., Oliver, M.A., 2007. Geostatistics for Environmental Scientists. John Wiley & Sons.
- 5. Clark, I., Harper, W.V., 2000. Practical Geostatistics 2000; Ecosse North America Lie. Columbus Ohio, USA, p.342.
- 6. Cressie, N., 1993. Statistics for Spatial Data, Revised Edition; John Wiley & Sons Press, New York, USA, p. 928.
- 7. Wackernagel, H., 2010. Multivariate Geostatistics, 3nd Edition; Springer-Verlag, Deutch, p. 387.
- 8. Srivastava, R.M., 2013. Geostatistics: A Toolkit for Data Analysis, Spatial Prediction and Risk Management in the Coal Industry, International Journal of Coal Geology, vol.112, p.2–13.
- 9. Dag, A., Mert, B.A., 2008. Evaluating Thickness of Bauxite Deposit Using Indicator Geostatistics and Fuzzy Estimation, Resource Geology, 58(2), p. 188-195.
- 10. Mert, B.A., Dag, A., 2015. Development of a GIS-based Information System for Mining Activities: Afsin-Elbistan Lignite Surface Mine Case Study, International Journal of Oil, Gas and Coal Technology 23, 9(2), p. 192-214.
- 11.Chiles, J. P., Delfiner, P., 2009. Geostatistics: Modeling Spatial Uncertainty, vol. 497. John Wiley & Sons.
- 12. Deutch, C.V., Journal, A.G., 1997. GSLIB: Geostatistical Software Library and User’s Guide, 2nd Edition; Oxford University Press, New York, USA, p.369.
- 13.Cressie, N., 1988. Spatial Prediction and Ordinary Kriging, Mathematical Geology, 20(4), p.405-421.
Ayrıntılar
| Bölüm | Makaleler |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 15 Aralık 2016 |
| Yayımlandığı Sayı | Yıl 2016 Cilt: 31 Sayı: 2 |