Exploitation
H. Moini; F. Mohammad Torab
Abstract
Kriging is an advanced geostatistical procedure that generates an estimated surface or 3D model from a scattered set of points. This method can be used for estimating resources using a grid of sampled boreholes. However, conventional ordinary kriging (OK) is unable to take locally varying anisotropy ...
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Kriging is an advanced geostatistical procedure that generates an estimated surface or 3D model from a scattered set of points. This method can be used for estimating resources using a grid of sampled boreholes. However, conventional ordinary kriging (OK) is unable to take locally varying anisotropy (LVA) into account. A numerical approach has been presented that generates an LVA field by calculating the anisotropy parameters (direction and magnitude) in each cell of the estimation grid. After converting the shortest anisotropic distances to Euclidean distances in the grid, they can be used in variography and kriging equations (LVAOK). The ant colony optimization (ACO) algorithm is a nature-inspired metaheuristic method that is applied to extract image features. A program has been developed based on the application of ACO algorithm, in which the ants choose their paths based on the LVA parameters and act as a moving average window on a primary interpolated grid. If the initial parameters of the ACO algorithm are properly set, the ants would be able to simulate the mineralization paths along continuities. In this research work, Choghart iron ore deposit with 2,447 composite borehole samples was studied with LVA-kriging and ACO algorithm. The outputs were cross-validated with the 111,131 blast hole samples and the Jenson-Shannon (JS) criterion. The obtained results show that the ACO algorithm outperforms both LVAOK and OK (with a correlation coefficient value of 0.65 and a JS value of 0.025). Setting the parameters by trial-and-error is the main problem of the ACO algorithm.
Exploitation
B. Sohrabian; R. Mikaeil; R. Hasanpour; Y. Ozcelik
Abstract
The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be ...
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The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be estimated at unknown locations. Cokriging has been traditionally used in the estimation of spatially cross-correlated variables. However, it can face unsolvable matrices in its algorithm. An alternative to cokriging is to transform variables into spatially orthogonal factors, and then to apply kriging to them. Independent Component Analysis (ICA) is one of the methods that can be used to generate these factors. However, ICA is applicable to zero lag distance so that using methods with distance parameter in their algorithms would be advantageous. In this work, Minimum Spatial Cross-correlation (MSC) was applied to six mechanical properties of Cubuk andesite quarry located in Ankara, Turkey, in order to transform them into approximately orthogonal factors at several lag distances. The factors were estimated at 1544 (5 m × 5 m) regular grid points using the kriging method, and the results were back-transformed into the original data space. The efficiency of the MSC-kriging was compared with Independent Component kriging (IC-kriging) and cokriging through cross-validation test. All methods were unbiased but the MSC-kriging outperformed the IC-kriging and cokriging because of having the lowest mean errors and the highest correlation coefficients between the estimated and the observed values. The estimation results were used to determine the most profitable blocks and the optimum direction of extraction.
H. Molayemat; F. Mohammad Torab
Abstract
Coalbed methane (CBM) plays an important role in coal mining safety and natural gas production. In this work, The CBM potential of B2 seam in Parvadeh IV coal deposit, in central Iran, was evaluated using a combination of local regression and geostatistical methods. As there were 30 sparse methane sampling ...
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Coalbed methane (CBM) plays an important role in coal mining safety and natural gas production. In this work, The CBM potential of B2 seam in Parvadeh IV coal deposit, in central Iran, was evaluated using a combination of local regression and geostatistical methods. As there were 30 sparse methane sampling points in the Parvadeh IV coal deposit, no valid variogram was achieved for the methane content. A multivariate adaptive regression splines (MARS) model was used to reproduce the methane content data based on seam depth, thickness, and ash content. The MARS model results were used in ordinary kriging to estimate the methane content in all mine blocks. A combination of MARS modeling and ordinary kriging in CBM studies is introduced, for the first time, in this paper. The results obtained show that high methane zones are located in the central and south western parts of the deposit. The in situ CBM potential varies from 6.0 to 16.1 m3/t, and it was estimated to be 1.39 billion m3 at the average depth of 267 m in an area of 86.55 km2. Although this volume is remarkable, little is known as how much of this resource is actually producible. Consequently, high methane-bearing zones are highly recommended for further studies as a source of natural gas for extraction and reducing the hazards and explosion risks of underground coal mining.
