The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, having geological and spatial meaning. Its importance lies in the fact that the quality of the estimation techniques, and therefore, the correct quantification of the mineral resource, will improve in geostatistically stationary areas. The present study seeks to define geostatistical domains of estimation for a mineral grade, using a non-traditional approach based on the k-prototype clustering algorithm. This algorithm is based on the k-means paradigm of unsupervised machine learning, but it is exempt from the one-time restriction on numeric data. The latter is especially convenient, as it allows the incorporation of categorical variables such as geological attributes in the grouping. The case study corresponds to a hydrothermal gold deposit of high sulfidation, located in the southern zone of Peru, where estimation domains are defined from a historical record of data recovered from 131 diamond drill holes and 37 trenches. The characteristics directly involved were the gold grade (Au), silver grade (Ag), type of hydrothermal alteration, and type of mineralization. The results obtained showed that clustering with k-prototypes is an efficient approach and can be used as an alternative or complement to the traditional methodology.
This work presents the results obtained from a spatial modeling and analysis process on pollutants measured in the air through forty-three monitoring stations located in the three provinces of the Basque Autonomous Community (Spain). The pollutants measured correspond to the set of nitrogen oxides (nitric oxide, NO; nitrogen dioxide, NO 2 ; and nitrogen oxides, NO x ) and atmospheric particulate matter with a diameter less than or equal to 10 micrometers (PM 10 ). The objective of this work was to generate a map of the pollutants that exhaustively covers the entire area of the Basque Autonomous Community using geostatistical techniques, in such a way that it serves as a basis for short and midterm environmental studies.
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