Total sulfur variability analysis of coal deposits using ordinary kriging estimation

2021 ◽  
Author(s):  
Yuniar Siska Novianti ◽  
Romla Noor Hakim ◽  
Nurhakim ◽  
Hafidz Noor Fikri
Keyword(s):  
Ordinary Kriging ◽  
Total Sulfur ◽  
Variability Analysis ◽  
Coal Deposits ◽  
Kriging Estimation

scholarly journals PERBANDINGAN INTERPOLASI SPASIAL DENGAN METODE ORDINARY DAN ROBUST KRIGING PADA DATA SPASIAL BERPENCILAN (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2012 ◽  
Author(s):  
NI MADE SUMA FRIDAYANI ◽  
I PUTU EKA NILA KENCANA ◽  
KOMANG GDE SUKARSA
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Control Point ◽  
Mean Absolute Deviation ◽  
Kriging Method ◽  
Absolute Deviation ◽  
Record Keeping ◽  
Kriging Estimation ◽  
Extreme Observation

Kriging as optimal spatial interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values that may be caused by faulty record keeping, improper calibration equipment or other posibbilities. Development of Ordinary Kriging method is Robust Kriging which transforms weight of clasic variogram thus become variogram that robust to outlier. The spatial data that used in this research is the spatial data that contains outliers and meet the assumptions of Ordinary Kriging. The analysis showed that the estimation value of Ordinary Kriging and Robust Kriging method is not much different in terms of Mean Absolute Deviation values which generated by both methods. An increase value of Mean Absolute Deviation on Robust Kriging estimation does not indicate that the Ordinary Kriging method is more precise than Robust Kriging method in the rainfall estimates of Amlapura control point remind that Robust Kriging does not eliminate the data of observation such as the Ordinary Kriging method. In general, Ordinary Kriging and Robust Kriging method can estimate the rainfall value of Amlapura control point quite well although it is not able to cover the changes in rainfall value that occurs due to the behavior geographic data.

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

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Bayram Ali Mert ◽  
Ahmet Dag
Keyword(s):  
Ordinary Kriging ◽  
Three Dimensional ◽  
Theoretical Models ◽  
Experimental Models ◽  
Oil Field ◽  
Kriging Interpolation ◽  
Parameter Distribution ◽  
Source Codes ◽  
Log Normal ◽  
Kriging Estimation

AbstractIn 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:

A geostatistical approach to cork production sampling estimation in Quercus suber forests

2005 ◽  
Vol 35 (12) ◽  
pp. 2787-2796 ◽  
Author(s):  
Fernando Montes ◽  
María José Hernández ◽  
Isabel Cañellas
Keyword(s):  
Ordinary Kriging ◽  
Measurement Errors ◽  
Quercus Suber ◽  
Indicator Kriging ◽  
Surface Distribution ◽  
Geostatistical Approach ◽  
Cork Production ◽  
Cork Thickness ◽  
Productive Area ◽  
Kriging Estimation

The estimation of cork production in cork oak (Quercus suber L.) forests is complex because of the high heterogeneity of stripped surface distribution (the variable used to quantify cork production) and the importance of cork thickness estimation as a determining factor of cork quality. In this study, the different sources of variation in stripped surface ([Formula: see text]d) estimation and the effects of the spatial structure of the variance were analysed. When indicator kriging was used to determine the cork productive area, ordinary kriging and kriging with measurement errors gave better estimations of [Formula: see text]d (ordinary block kriging estimation of 156.16 m2/ha and standard errors (SE) of 16.40 and 15.7 m2/ha, respectively) than the design-based approach for the whole forest area (66.37 m2/ha, SE = 11.34 m2/ha). The SE lying in the second-stage design was 4.93 m2/ha. The ordinary kriging prediction of cork thickness using an XY(λZ) variogram, where λ is the anisotropy coefficient of the Z axis, gives a smaller SE and less bias than the kriging prediction with the XY variogram (for a mean estimation of 21.91 mm, SE = 3.90 and 4.16 mm, respectively, and sum of errors of 0.42 and 0.85 respectively).

MINING AT HORIZONTAL AND SLIGHTLY INCLINED COAL DEPOSITS UNDER THE FAN SYSTEM

2017 ◽  
Vol 12 (38) ◽  
pp. 144-152
Author(s):  
P.V. Katyshev ◽  
◽  
V.E. Kislyakov ◽  
Keyword(s):  
Coal Deposits
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