Evaluation of Tailings from a Porphyry Copper Mine based on Joint Simulation of Contaminants

Natural Resources Research - Tailings from porphyry copper mines contain environmentally harmful amounts of elements such as copper, molybdenum, lead and cobalt. Geostatistical simulation of...     

A Simulated Annealing-Based Algorithm to Locate Additional Drillholes for Maximizing the Realistic Value of Information

Locating additional drillholes based on information gathered from the initial drilling is a very difficult decision-making step in the process of detailed explorations. The most appropriate locations for additional drillholes are those wherein the information gathered from drilling has more value compared to that from other locations. From among the common methods proposed in information systems for measuring the information value, use of the “realistic value” is a very practical one. The realistic value of information is derived from measuring the differences in the decision makers’ performances when provided with different information sets. On this basis, a mathematical model has been proposed in this paper for optimal location of additional drillholes where the information gathered from drillholes has the highest possible value. Due to the combinatorial nature of this model, use has been made of a simulated annealing-based algorithm for its solution. The proposed model has been applied in Sungun copper deposit for locating additional drillholes; results have revealed that the model is valid.     

An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit

Sungun porphyry copper deposit is in East Azarbaijan province, NW of Iran. There exist four hypogene alteration types in Sungun: potassic, propylitic, potassic–phyllic, and phyllic. Copper mineralization is essentially associated more with the potassic and less with the phyllic alterations and their separation is, therefore, quite important. This research has tried to separate these two alteration zones in Sungun porphyry copper deposit using the Support Vector Machine (SVM) method based on the fluid inclusion data, and seven variables including homogenization temperatures, salinity, pressure, depth, density and the Cu grade have been measured and calculated for each separate sample. To apply this method, use is made of the radial basis function (RBF) as the kernel function. The best values for λ and C (the most important SVM parameters) that perform well in the training and test data are 0.0001 and 1, respectively. If these values for λ and C are applied, the phyllic and potassic alteration zones in the training and test data will be separated with an accuracy of about 95% and 100%, respectively. This method can help geochemists in separating the alteration zones because classifying and separating samples microscopically is not only very hard, but also quite time and money consuming.     

FuzzyKrig: a comprehensive matlab toolbox for geostatistical estimation of imprecise information

Using kriging has been accepted today as the most common method of estimating spatial data in such different fields as the geosciences. To be able to apply kriging methods, it is necessary that the data and variogram model parameters be precise. To utilize the imprecise (fuzzy) data and parameters, use is made of fuzzy kriging methods. Although it has been 30 years since different fuzzy kriging algorithms were proposed, its use has not become as common as other kriging methods (ordinary, simple, log, universal, etc.); lack of a comprehensive software that can perform, based on different fuzzy kriging algorithms, the related calculations in a 3D space can be the main reason. This paper describes an open-source software toolbox (developed in Matlab) for running different algorithms proposed for fuzzy kriging. It also presents, besides a short presentation of the fuzzy kriging method and introduction of the functions provided by the FuzzyKrig toolbox, 3 cases of the software application under the conditions where: 1) data are hard and variogram model parameters are fuzzy, 2) data are fuzzy and variogram model parameters are hard, and 3) both data and variogram model parameters are fuzzy.