Exploration
H. Sabeti; F. Moradpouri
Abstract
The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). ...
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The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). The main advantage of the DSS algorithm against the SGS algorithm is that in the DSS algorithm no Gaussian transformation of the original data is made. In this work, these two simulation algorithms are explained, and their applications to a 3D spatial dataset are deeply investigated. The dataset consists of the porosity values of 16 vertical wells extracted from an actual cube obtained by a seismic inversion process. One well data is excluded from the simulation process for the blind well test. Comparison between the histograms show that the histogram reproduction is slightly better for the SGS algorithm, although the population reproductions are the same for both SGS and DSS results. The DSS algorithm reproduce the mean of input data closer to the mean of well data compared to that of the SGS algorithm. Considering one realization from each simulation algorithm, the RMS error corresponding to all simulated cells against the real values is approximately equal for both algorithms. On the other hand, the error show a slightly less value when the mean of 100 realizations of the DSS result is considered.
M. Taghvaeenejad; M.R. Shayestefar; P. Moarefvand
Abstract
At different stages of mining, we always face a degree of uncertainty. Some of these uncertainties, such as the amount of reserve and grade of the deposit, are due to the inherent changes in the deposit and directly affect the technical and economic indicators of the deposit. On the other hand, the heavy ...
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At different stages of mining, we always face a degree of uncertainty. Some of these uncertainties, such as the amount of reserve and grade of the deposit, are due to the inherent changes in the deposit and directly affect the technical and economic indicators of the deposit. On the other hand, the heavy costs of the exploration sector often limit the amount of exploratory information, which necessitates the use of accurate estimation methods. In this work,we examines the modeling and estimation results using the conventional and simple kriging methods and the effects of the diverse indicators used in the classification of mineral storages or the parameters defining these indices. 127 exploratory boreholes with an average depth of 95 m are used to build the block model of the deposit in the Data Mine software. After the statistical studies, the 3D variographic studies are performed in order to identify the anisotropy of the region. A grade block model is constructed using the optimal variogram parameters.Then, using various methods to estimate the block model uncertainty including the kriging estimation variance, block error estimation, kriging efficiency and slope of regression, the mineral reserves are classified according to the JORC standard code. Based on different cut-off grades, the tonnage and average grade are calculated and plotted. In this work, an innovative quantitative method based on the grade-number and grade-volume fractal model is used to indicate the classification of mineral reserves. The use of fractal patterns due to the amplitude of the variation is greater and more important than the standard and provides us with a better understanding of the deposit changes per block. The existence of a minimal difference between the use of the standard and fractal patterns in the slope of the regression method indicates less error and is a proof of more reliable results.
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.
Omid Asghari
Abstract
Post-mineralization activities may cause difficulties in the process of ore modeling in porphyry deposits. Sungun, NW Iran, is one of the porphyry copper deposits, in which dyke intrusions have made ore modeling more complicated than expected. Among different kinds of dykes, two types were chosen and ...
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Post-mineralization activities may cause difficulties in the process of ore modeling in porphyry deposits. Sungun, NW Iran, is one of the porphyry copper deposits, in which dyke intrusions have made ore modeling more complicated than expected. Among different kinds of dykes, two types were chosen and the consequent geostatistical analyses were applied on. In this study, simple directional variograms were used for extracting relevant information from dyke systems on which the Sequential Indicator Simulations were applied consequently. One hundred realizations were produced on the simulation grid considering the anisotropy characteristics and E-type map has been provided averaging all realizations. Moreover, a binary state between dyke and non-dyke environments was produced putting threshold on the E-type grid node values to discriminate the ore from barren dykes. Hole-effect models were fitted to the empirical variograms perpendicular to the dyke strike. Dimensional information was elicited from these models and the results were compared with the previously carried out geological investigations, and finally a good numerical match was found between these two sources of information.
