Geostatistical estimation of coal quality variables by using covariance matching constrained kriging

ERTUNÇ G., TERCAN A. E., HİNDİSTAN M. A., ÜNVER B., ÜNAL S., ATALAY F., ...Daha Fazla

INTERNATIONAL JOURNAL OF COAL GEOLOGY, cilt.112, ss.14-25, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 112
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.coal.2012.11.014
  • Dergi Adı: INTERNATIONAL JOURNAL OF COAL GEOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.14-25
  • Anahtar Kelimeler: Covariance matching constrained kriging, Variogram, Block kriging, Block model
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Covariance matching constrained kriging is a technique originally developed for estimating linear or nonlinear functional of the variable of interest. Practically this may be regarded as a hybrid system that considers local accuracy property in kriging and preservation of spatial variability in stochastic simulation. The method estimates the unknown vector by adding the constraint to match the variance-covariance matrix of estimated values with the variance-covariance matrix of actual values under unbiasedness constraint.

Covariance matching constrained kriging is a technique originally developed for estimating linear or nonlinear functional of the variable of interest. Practically this may be regarded as a hybrid system that considers local accuracy property in kriging and preservation of spatial variability in stochastic simulation. The method estimates the unknown vector by adding the constraint to match the variance–covariance matrix of estimated values with the variance–covariance matrix of actual values under unbiasedness constraint.

This study applies the method to a certain section of a lignite deposit which is subjected to severe tectonic activity. Lower heating value, ash content, and moisture content are estimated by covariance matching constrained kriging and ordinary kriging and also simulated by sequential Gaussian simulation. Comparisons between these methods show that covariance matching constrained kriging reproduces the spatial variability better than ordinary kriging and gives more accurate estimates than conditional simulation. Covariance matching constrained kriging can be used when spatial variability of the estimations is important as well as local accuracy.