Exploitation
Hamidreza Ramazi; Moslem Jahantigh
Abstract
The present study aims to compare data-driven and knowledge-driven methods in weighting exploration witness layers in porphyry copper potential in the study area of Shahr-e-Babak, Iran. Initially, eight exploration layers including geochemical, geological, airborne magnetic, and remote sensing data were ...
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The present study aims to compare data-driven and knowledge-driven methods in weighting exploration witness layers in porphyry copper potential in the study area of Shahr-e-Babak, Iran. Initially, eight exploration layers including geochemical, geological, airborne magnetic, and remote sensing data were preprocessed and prepared. Then, these layers were transferred to an equal distance of zero and one using a fuzzy function. The weighting of exploration witness layers in the study area was done using three methods: AHP, random forest algorithm, and area prediction method. Then, the weighted layers were combined using the MABAC multivariate decision-making method and three mineral potential models were formed for the study area. The produced models were compared using the main characteristic curve diagram. It was found that weighting using a data-driven method has more desirable results than a knowledge-driven method. Also, weighting exploration layers by random forest algorithm has more desirable results than the area prediction method. The better results of the random forest algorithm are because in this algorithm, weighting is based on mineralized and non-mineralized points, while in the area prediction method, weighting is only based on outcrops in the study area. This method can be implemented with appropriate accuracy in areas with limited training data, along with knowledge-based methods for weighting exploratory evidential layers.
Rock Mechanics
M. Nikkhah; M. A. Ghasvareh; N. Farzaneh Bahalgardi
Abstract
In general, underground spaces are associated with high risks because of their high uncertainty in geotechnical environments. Since most accidents and incidents in these structures are often associated with uncertainty, the development of risk analysis and management methods and prevention of accidents ...
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In general, underground spaces are associated with high risks because of their high uncertainty in geotechnical environments. Since most accidents and incidents in these structures are often associated with uncertainty, the development of risk analysis and management methods and prevention of accidents are essential. A deeper recognition of the factors affecting the implementation process can pave the way for this purpose. Risk rating of projects is a key part of the risk assessment stage in the risk management process of each project. Various multi-criteria decision-making methods, as quantitative approaches, are used to allow them to be used in the risk rating issue of each project. In this work, a new model is provided for risk management of Mashhad Urban Railway Line 3 using the game theory and multi-criteria decision-making methods. Based on the answers of the specialists and experts to the prepared questionnaires, various risk groups identified using the TOPSIS and AHP multi-criteria decision-making methods are ranked. Accordingly, the group of economic risks, as the most important risk and social risk group, is ranked as the least significant in both methods. In the following, the appropriate response to the main risks of the ratings is proposed based on the modeling of the game theory, and ranked in terms of importance. Also the worst risk scenario in the project is identified, and the appropriate responses for this state are also expressed in order of importance. The results obtained indicate that the risk of financing problems is the most significant risk, and other risks are ranked in terms of importance in the next ranks. Additionally, the use of new financing methods at times of credit scarcity and project financial problems is also considered as the most important response to the risk in this project.
Exploitation
S. Saadat
Abstract
Motivated by the recent successful results of using GIS modeling in a variety of problems related to the geosciences, some knowledge-based methods were applied to a regional scale mapping of the mineral potential, special for Cu-Au mineralization in the Feyz-Abad area located in the NE of Iran. Mineral ...
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Motivated by the recent successful results of using GIS modeling in a variety of problems related to the geosciences, some knowledge-based methods were applied to a regional scale mapping of the mineral potential, special for Cu-Au mineralization in the Feyz-Abad area located in the NE of Iran. Mineral Prospectivity Mapping (MPM) is a multi-step process that ranks a promising target area for more exploration. In this work, five integration methods were compared consisting of fuzzy, continuous fuzzy, index overlay, AHP, and fuzzy AHP. For this purpose, geological maps, geochemical samples, and geophysics data were collected, and a spatial database was constructed. ETM + images were used to extract the hydroxyl and iron-oxide alterations, and to identify the linear and fault structures and prospective zones in regional scale; ASTER images were used to extract SiO2 index, kaolinite, chlorite, and propylitic alterations in a district scale. All the geological, geochemical, and geophysical data was integrated for MPM by different analysis. The values were determined by expert knowledge or logistic functions. Based upon this analysis, three main exploration targets were recognized in the Feyz-Abad district. Based on field observation, MPM was proved to be valid. The prediction result is accurate, and can provide directions for future prospecting. Among all the methods evaluated in this work, which tend to generate relatively similar results, the continuous fuzzy model seems to be the best fit in the studied area because it is bias-free and can be used to generate reliable target areas.
