Mine Economic and Management
Sarina Akbari; Reza Ghezelbash; Hamidreza Ramazi; Abbas Maghsoudi
Abstract
Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies ...
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Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.
Exploration
Mobin Saremi; Abbas Maghsoudi; Reza Ghezelbash; mahyar yousefi; Ardeshir Hezarkhani
Abstract
Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were ...
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Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were employed in alignment with the conceptual model of these deposits. These layers encompass fault density, proximity to intrusive rocks, multi-element geochemical anomalies, and distances to phyllic and argillic alterations. The geochemical anomaly maps, recognized as the most effective layers, were generated through staged factor analysis (SFA) and the geochemical mineralization probability index (GMPI). Other layers were weighted using a logistic function, and their values were transformed into 0 -1 interval. Ultimately, to integrate the weighted layers, the fuzzy gamma operator and the geometric average method were applied. The normalized density index and prediction-area (P-A) plot were employed to evaluate the MPM models. The findings indicate that the developed models possess considerable validity and can be effectively utilized for planning future exploration endeavors.
N. Habibkhah; H. Hassani; A. Maghsoudi; M. Honarmand
Abstract
The Dehaj area, located in the southern part of the Urumieh-Dokhtar magmatic belt, is a well-endowed terrain hosting a number of world-class porphyry copper deposits. These deposits are all hosted in an acidic to intermediate volcano-plutonic sequence greatly affected by various types of the hydrothermal ...
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The Dehaj area, located in the southern part of the Urumieh-Dokhtar magmatic belt, is a well-endowed terrain hosting a number of world-class porphyry copper deposits. These deposits are all hosted in an acidic to intermediate volcano-plutonic sequence greatly affected by various types of the hydrothermal alterations, whether argillic, phyllic or propylitic. Although there are a handful of hitherto-discovered porphyry copper deposits in the area, the geological setting of the area suggests the possibility of finding further deposits. The recognition and delineation of the hydrothermal alterations can pave the way for the discovery of further potential zones that possibly host the porphyry copper deposits. The current work proposes a hybrid methodology applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery by combining the application of dimension reduction and fractal techniques to delineate the hydrothermally-altered zones In order to reduce the dimensionality of multi-band ASTER data, Robust Principal Component Analysis (RPCA) was employed to elicit the traces of hydrothermally-related mineral assemblages including illite, sericite, quartz, kaolinite, epidote, and chlorite. Highlighting the existence of the aforementioned minerals, the extracted components require interpretation, i.e. a boundary is required to constraint the hydrothermally affected zones from the rest of the geological units. In order to tackle such a challenge, the authors introduce the concept of value-pixel fractal technique for the extracted principal components. The Prediction-Area (P-A) plot is used for the validation, which shows that the identified alterations correlate with the mineralization. The results obtained are verified by a geological survey, where a number of samples are collected from the delineated zones. The samples are analyzed by the XRD techniques, finding that this work is successful in classifying the hydrothermally-altered zones.
Exploitation
O. Gholampour; A. Hezarkhani; A. Maghsoudi; M. Mousavi
Abstract
This paper presents a quantitative modeling for delineating alteration zones in the hypogene zone of the Miduk porphyry copper deposit (SE Iran) based on the core drilling data. The main goal of this work was to apply the Ordinary Kriging (OK), Artificial Neural Networks (ANNs), and Concentration-Volume ...
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This paper presents a quantitative modeling for delineating alteration zones in the hypogene zone of the Miduk porphyry copper deposit (SE Iran) based on the core drilling data. The main goal of this work was to apply the Ordinary Kriging (OK), Artificial Neural Networks (ANNs), and Concentration-Volume (C-V) fractal modelings on Cu grades to separate different alteration zones. Anisotropy was investigated and modeled based on calculating the experimental semi-variograms of Cu value, and then the main variography directions were identified and evaluated. The block model of Cu grade was generated using the kriging and ANN modelings followed by log-log plots of the C-V fractal modeling to determine the Cu threshold values used in delineating the alteration zones. Based on the correlation between the geological models and the results derived via C-V fractal modeling, Cu values less than 0.479% resulting from kriging modeling had more overlapped voxels with the phyllic alteration zone by an overall accuracy (OA) of 0.83. The spatial correlation between the potassic alteration zone in a 3D geological model and the high concentration zones in the C-V fractal model showed that Cu values between 0.479% and 1.023%, resulting from kriging modeling, had the best overall accuracy (0.78). Finally, based on the correlation between classes in the binary geological and fractal models of the hypogene zone, this research work showed that kriging modeling could delineate the phyllic (with lower grades) and potassic (with higher grades) alteration zones more effectively compared with ANNs.
H. Azmi; P. Moarefvand; A. Maghsoudi
Abstract
Delineation of oxide and sulfide zones in mineral deposits, especially in gold deposits, is one of the most essential steps in an exploration project that has been traditionally carried out using the drilling results. Since in most mineral exploration projects there is a limited drilling dataset, application ...
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Delineation of oxide and sulfide zones in mineral deposits, especially in gold deposits, is one of the most essential steps in an exploration project that has been traditionally carried out using the drilling results. Since in most mineral exploration projects there is a limited drilling dataset, application of geophysical data can reduce the error in delineation of the sulfide and oxide zones. For this purpose, we produced a 3D model of Induced Polarization (IP) data using the ordinary kriging technique. Then the modelling results were compared with the drilling data. The results obtained showed that the 3D geophysical models would properly delineate the sulfide and oxides zones. This work presents a new application of the IP results for separation of these zones. In addition, the conducted variography in this work suggests reducing the profile spacing of dipole-dipole IP arrays down to 25 m. This would properly enrich the integration of geophysical and geological results in the modelling of gold deposits.
