Open-pit mining method has severe environmental impacts which should be prevented, monitored, controlled, and reduced by mined-land reclamation process. After mine closure, a permanent post-mining land use should be implemented as an appropriate choice for using different sections of mined land. The most appropriate alternative of post-mining land use for each section of mined land is presented as the optimum post-mining land use. Pit area among different sections of mined land has more significant effects on the environment and also on defining the optimum post-mining land use for other sections of mined land. Though there are several alternatives and criteria for defining the optimum post-mining land use, the multi-attribute decision-making methods can be efficient techniques in this regard. The nature of the effective parameters used for defining the optimum post-mining land use is the same as Fuzzy numbers including incremental changes without definite limits. Thus, application of the Fuzzy multi-attribute decision-making modeling can produce more reliable results than that of other techniques. As well, pair-wise comparisons and judgments through Fuzzy numbers have proper consistency with the nature of the effective parameters; therefore, a model is developed to attain the optimum post-mining land use for pit area through Fuzzy analytical hierarchy processing. As a case study, the model was implemented in Sungun copper mine in the Northwest of Iran. Forestry-lumber production was defined as the optimum post-mining land use containing the greatest relative importance coefficient 3.019 for the pit area in this mine.
Rock mass characterization is one of the most important parameters affecting the underground mine design. This study deals with the prediction of rock mass quality using geo-statistical estimator in Anguran underground mine at the level +2740. For this, a database consisting of 427 Q-based rock mass quality data sets was developed during the development of mine drifts. Accordingly, data were analyzed and checked for normality, trend and anisotropy. Analysis on Q-data showed that: 1-they do not have a normal distribution, 2there are neither global nor local outliers in data 3-the data seem to exhibit a trend. In this study, the Universal Kriging was used due to existing trend in datasets. Taking into consideration five estimation error evaluation criteria, the best Variogram model was selected among three models: exponential, spherical and Gaussian. The results showed that spherical variogram model provides the best fit to the data's spatial structure. Cross validation showed high accuracy level for performance of geo-statistical estimator. Accordingly, the rock mass quality map for the area under study built in ArcGIS environment. The analysis results of final rock mass quality map revealed that about 53% of under study area has poor to extremely poor rock mass condition, 8% has fair and 39% has good to extremely good rock mass condition.
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