An interactive demo that shows the Kriging system and its solution based on some samples in two-dimensions 🚀
The 100% Python geostatistical tool 😄
Exequiel Sepúlveda is a postdoctoral researcher fellow at the School of Civil, Environmental and Mining Engineering in The University of Adelaide. His research interests include machine learning, geostatistics, metaheuristics optimisation, high performance computing and digital mining. He loves programming.
After he completed his studies in Computer Science, Exequiel had almost 20 years experience in software engineering, working in industries in the financial, insurances and energy sectors, among others. That experience brought him to work in the Mining School at The University of Chile, where his became a researcher assistant in the ALGES laboratory. That was the beginning of his interest of doing a PhD in geometallurgy.
PhD in Geometallurgy - Mining Engineering, 2018
The University of Adelaide
BSc in Computer Science, 1996
University of Chile
95% confidence interval
Rapid Resource Model Updating
Heterogeneity Index for Grade Engineering
The access to real geometallurgical data is very limited in practice, making it difficult for practitioners, researchers and students to test methods, models and re- produce results in the field of geometallurgy. The aim of this work is to propose a methodology to simulate geometallurgical data with geostatistical tools preserving the coherent relationship among primary attributes, such as grades and geological attributes, with mineralogy and some response attributes, for example, grindability, throughput, kinetic flotation performance and recovery. The methodology is based in three main components: (i) definition of spatial relationship between Geomet- allurgical units, (ii) co-simulation of regionalized variables with geometallurgical coherence, and (iii) simulation of georeferenced drillholes based on geometrical and operational constraints. The simulated geometallurgical drillholes generated look very realistic, and they are consistent with the input statistics, coherent in terms of geology and mineralogy, and produce realistic processing metallurgical perfor- mance responses. These simulations can be used for several purposes, for example, benchmarking geometallurgical modelling methods and mine planning optimiza- tion solvers, or performing risk assessment under different blending schemes. 1