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
A. Aryafar; H. Moeini
Abstract
Anomaly separation using stream sediment geochemical data has an essential role in regional exploration. Many different techniques have been proposed to distinguish anomalous from study area. In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial ...
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Anomaly separation using stream sediment geochemical data has an essential role in regional exploration. Many different techniques have been proposed to distinguish anomalous from study area. In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial neural network, was used to recognize the mineral potential area in Korit 1:100000 sheet, located 15 km south of Tabas, South Khorasan Province (East of Iran). For this purpose, 470 geochemical stream sediment samples were collected from the study area and analyzed for 36 elements. In order to achieve the goal, in the first step, the robust factor analysis on compositional data was applied to reduce the data dimension and to limit the multivariate analysis by selecting the main components of mineralization. In this procedure, the third factor (out of 6) consisting of Cu, Pb, Zn, Sn, and Sb, related to the metallogenic properties, was considered as the input set in CRBM. In continuation, the CRBM structure with the best efficiency after trying different parameters was stabilized. High-identified error values or anomalies were exteracted using two different thresholds (ASC and ASE) after training with the whole data and reconstructing it by CRBM. The anomalies were then mapped. These indicated the promissing areas. The field studies and existing mining indices confirmly demonestrated the results obtained by CRBM.
H. Moeini; A. Aryafar
Abstract
Anomaly recognition has always been a prominent subject in preliminary geochemical explorations. Among the regional geochemical data processing, there are a range of statistical and data mining techniques as well as different mapping methods, which serve as presentations of the outputs. The outlier’s ...
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Anomaly recognition has always been a prominent subject in preliminary geochemical explorations. Among the regional geochemical data processing, there are a range of statistical and data mining techniques as well as different mapping methods, which serve as presentations of the outputs. The outlier’s values are of interest in the investigations where data are gathered under controlled conditions. These values in exploration geochemistry indicate the mineralization occurrences, and therefore, their identification is vital. Both the robust parametric (based on Mahalanobis distance) and non-parametric (based on depth functions) techniques have been developed for a multivariate outlier identification in geochemistry data. In this research work, we applied the local multivariate outlier identification approach to delineate the geochemical anomaly halos in the Hamich region, which is located in the SE of Birjand, South Khorasn province, East of Iran. For this purpose, 396 litho-geochemical samples that had been analyzed for 44 elements were used. The obtained results show a good agreement with the geological and mineral indices of Pb, Zn, and Cu in the southern part of the area. Such studies can be used by a project director to optimize the core drilling places in detailed exploration steps.
