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Comparison between moving windows statistical method and kriging method in coal resource estimation
Author(s) -
Irfan Marwanza,
Chairul Nas,
Masagus Ahmad Azizi,
J. H. Simamora
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/3/033016
Subject(s) - kriging , estimation , coal , resource (disambiguation) , environmental science , computer science , statistics , mathematics , geography , engineering , archaeology , computer network , systems engineering
The calculation of coal resource estimation is generally done by a conventional and nonconventional method. This research uses a nonconventional method of Moving Windows Statistical (MWS) and Kriging using 3-dimensional calculation boxes area with dimensions of 100 m x 100 m, 200 m x 200 m, and 300 m x 300 m, which is processed using MWS and SGems software. The desired value is the comparison of average coal thickness value produced from basic statistics, Inverse Distance Squared (IDS), and Kriging, and also the estimation of coal resources. By using MWS, the obtained average value of coal thickness for area 100 x 100, 200 x 200, and 300 x 300 are 6.40 m, 6.24 m, and 6.03 m, respectively. From IDS the value from 100 x100 box is 6.38 m, 200 x 200 is 6.16 m, and 300 x 300 is 5.94 m, while the calculation of Kriging using Sgems generates 5,77 m of coal thickness in 100 x100, 5.68 m in 200 x 200, and 5.8 m in 300 x 300. Coal resource estimations using these three different areas of the box have also been carried out, resulting 943,480 tons, 526.460 tons, and 327.450 tons of coal in 100 x 100, 200 x 200, and 300 x 300. In the comparison of the average value of coal thickness between values determined from basic statistics, IDS, and Kriging on each area it is found that the value did not change significantly but the value from the Kriging method is the most accurate because of the influence of spatial variation of data where one point with the other point influences each other.

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