Spectral analysis methods were used for detection of mineralogical features on a set of Enhanced Thematic Mapper Plus data of Behabad zone, central Iran. Several indicative minerals for hydrothermal alterations were identified in the study area. The spectra of unknowns were determined by comparing to USGS mineral spectral library. Different pre-processings and processings were performed to achieve the highest possible accuracy. These are the minimum noise fraction, the pixel purity index analysis, spectral feature fitting, spectral angle mapper and binary encoding. The results of spectral analysis, as a map of minerals abundances, along with the results of other Image processing methods such as least square fit, and Crosta method were integrated within ArcGIS Software to achive a potential map of hydrothermal alterations. The minerals: allanite, magnetite, alunite, clay minerals, and muscovite were therefore detected and mapped in this study. The detected alterations here highly match to the mineral concentrations which are formerly found and measured in the study area that emphasizes the reliability of the applied method.
Blasting is widely employed as an accepted mechanism for rock breakage in mining and civil activities. As an environmental side effect of blasting, flyrock should be investigated precisely in open-pit mining operations. This paper proposes a novel integration of artificial neural network and fuzzy cognitive map (FCM) with Z-number reliability information to predict flyrock distance in open-pit mine blasting. The developed model is called the artificial causality-weighted neural networks, based on reliability (ACWNNsR). The reliability information of Z-numbers is used to eliminate uncertainty in expert opinions required for the initial matrix of FCM, which is one of the main advantages of this method. FCM calculates weights of input neurons using the integration of nonlinear Hebbian and differential evolution algorithms. Burden, stemming, spacing, powder factor, and charge per delay are used as the input parameters, and flyrock distance is the output parameter. Four hundred sixteen recorded basting rounds are used from a real large-scale lead–zinc mine to design the architecture of the models. The performance of the proposed ACWNNsR model is compared with the Bayesian regularized neural network and multilayer perceptron neural network and is proven to result in more accurate prediction in estimating blast-induced flyrock distance. In addition, the results of a sensitivity analysis conducted on effective parameters determined the spacing as the most significant parameter in controlling flyrock distance. Based on the type of datasets used in this study, the presented model is recommended for flyrock distance prediction in surface mines where buildings are close to the blasting site.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.