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.
Exploration
F. Mirsepahvand; M.R. Jafari; P. Afzal; M. A. Arian
Abstract
The goal of this research work is to recognize the metallic mineralization potential in the Ahar 1:100,000 sheet (NW Iran) using the remote sensing data based on determination of the alteration zones. This area is located in the Ahar-Arasbaran metallogenic zone as a significant metallogenic zone in Iran ...
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The goal of this research work is to recognize the metallic mineralization potential in the Ahar 1:100,000 sheet (NW Iran) using the remote sensing data based on determination of the alteration zones. This area is located in the Ahar-Arasbaran metallogenic zone as a significant metallogenic zone in Iran and Caucasus. In this research work, the Landsat-7 ETM+ and advanced space borne thermal emission and reflection radiometer (ASTER) multispectral remote sensing data was interpreted by the least square fit (LS-Fit), spectral angle mapper (SAM), and matched filtering (MF) algorithms in order to detect the alteration zones associated with the metallic mineralization. The results obtained by these methods show that there are index-altered minerals for the argillic, silicification, advanced argillic, propylitic, and phyllic alteration zones. The main altered areas are situated in the SE, NE, and central parts of this region.
Exploration
N. Mahvash Mohammadi; A. Hezarkhani
Abstract
Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, ...
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Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for alteration classification by applying two new methods of machine learning, including Random Forest and Support Vector Machine. The 14 band ASTER and 19 derivative data layers extracted from ASTER including band ratio and PC imagery, are used as training datasets for improving the results. Comparison of analytical results achieved from the two mentioned methods confirmed that the SVM model has sufficient accuracy and more powerful performance than RF model for alteration classification in the study area.
Exploration
V. Adjiski; D. Mirakovski; Z. Despodov; S. Mijalkovski
Abstract
Auxiliary ventilation of the blind development heading in underground mines is one of the most challenging work activities amongst mining underground operations. The auxiliary forcing ventilation system provides positive pressure, cooling, controlling gas layering, and removing diesel fumes and dust ...
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Auxiliary ventilation of the blind development heading in underground mines is one of the most challenging work activities amongst mining underground operations. The auxiliary forcing ventilation system provides positive pressure, cooling, controlling gas layering, and removing diesel fumes and dust levels from development headings, stopes, and services facilities. The effectiveness of the auxiliary forcing ventilation system depends upon many system variables. Currently, no scientific models and calculations are available that can be used to estimate the optimal distance from the outlet of the auxiliary forcing ventilation system to the development heading in underground mines that can provide the most efficient ventilation close to the face of the heading. In this work, scenarios are developed and simulated with a validated CFD model inside the ANSYS Fluent software. In each scenario, the system parameters such as dead zone, mean age of air, and face velocity are calculated, which are later used in the optimization process. By examining these parameters at the development heading zone, we can quantify the effectiveness of the ventilation system and confirm that the system design meets the government regulations. This work is carried out using the k-epsilon realizable turbulent model inside the ANSYS Fluent software.
Exploration
A. Habibnia; Gh. R. Rahimipour; H. Ranjbar
Abstract
Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing ...
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Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing techniques in the analysis of stream sediment geochemical datasets and ASTER satellite images. The processing methods have led to identification of eight new prospective areas. These methods are aimed at providing univariate geochemical maps. The stream sediment geochemical mapping for Cu and Mo was performed by the sample catchment basin approach. The results derived from this approach have been mapped in four classes associated with the first quartile, third quartile and threshold values obtained from Median Absolute Deviation method. False-colour composite and band ratio techniques were adopted as two well-known methods for the processing of an ASTER scene spanning the study area. Eight new targets for possible mineralization have been resulted from geochemical data analyses. Image processing techniques on the ASTER multispectral data have also revealed widespread hydrothermal alterations associated with the known porphyry copper deposits and the new prospects.
Exploration
M. Honarmand; H. Ranjbar; H. Shahriari; F. Naseri
Abstract
This research was performed with the objective of evaluating the accuracy of spectral angle mapper (SAM) classification using different reference spectra. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital images were applied in the SAM classification in order to map the ...
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This research was performed with the objective of evaluating the accuracy of spectral angle mapper (SAM) classification using different reference spectra. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital images were applied in the SAM classification in order to map the distribution of hydrothermally altered rocks in the Kerman Cenozoic magmatic arc (KCMA), Iran. The study area comprises main porphyry copper deposits such as Meiduk and Chahfiroozeh. Collecting reference spectra was considered after pre-processing of ASTER VNIR/SWIR images. Three types of reference spectra including image, USGS library, and field samples spectra were used in the SAM algorithm. Ground truthing and laboratory studies including thin section studies, XRD analysis, and VNIR-SWIR reflectance spectroscopy were utilized to verify the results. The accuracy of SAM classification was numerically calculated using a confusion matrix. The best accuracy of 74.01% and a kappa coefficient of 0.65 were achieved using the SAM method using field samples spectra as the reference. The SAM results were also validated with the mixture tuned matched filtering (MTMF) method. Field investigations showed that more than 90% of the known copper mineralization occurred within the enhanced alteration areas.
