Recent Submissions

  • Multiple-point statistics: tools and methods 

    Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2020)
    Geostatistical simulation is used in uncertainty quantification and management in many fields of Earth Sciences. Conventional tools account only for two-points statistics and are unable to capture complex features. ...
  • Predictive Geometallurgy and Geostatistics Lab: Annual Report 2020 

    Altinpinar, Mehmet; Avalos, Sebastian; Casson, David; Castro, Julio; Cevik, S. Ilkay; Faraj, Fouad; Garrido, Mauricio; Moraga, Carlos; Kracht, Willy; Ortiz, Julian M.; Riquelme, Alvaro I.; Sari, Yuksel Asli; Townley, Brian (Queen's University, 2020)
    The research developed during the last year is summarized in the Annual Report 2020. This report is made available to the general public for reference. Collaboration from other research groups and from industry is encouraged. ...
  • A Short Note on a Multi-Gaussian Model for Multivariate Estimation and Simulation 

    Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2020)
    We show that multivariate non-linear features can be reconstructed in a simple and straightforward way by mapping the original p-variate cumulative distribution function with a p-variate Gaussian distribution, equipped ...
  • A guide for pit optimization with pseudoflow in python 

    Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2020)
    The Pseudoflow algorithm is used to outline the ultimate pit limit by finding the maximum net value of the blocks extracted, while respecting precedences for their extraction. It works by modelling the blocks as a direct ...
  • Ultimate pit policy via sequential Gaussian simulation 

    Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2020)
    In open pit projects, defining the limits of an ultimate pit is required to quantify the total mineral reserves. A single economic block model assessment along with spatial constrains serve as input to conventional ultimate ...
  • Validation of geostatistical simulations of porphyry deposit through geological approach using ioGAS 

    Garrido, Mauricio; Townley, Brian; Ortiz, Julian M. (Queen's University, 2020)
    Geostatistical simulations of ore body deposits are useful to quantify risk and uncertainty, testing mine planing algorithms, generating drill holes databases, among others. Geostatistical simulations are a common tool to ...
  • Synthetic high-resolution ore deposit model and mine plan 

    Altinpinar, Mehmet; Sari, Yuksel Asli; Ortiz, Julian M. (Queen's University, 2020)
    Decision making in the mining value chain is hindered by the variability of the rock properties and the uncertainty associated with their estimation in the blocks mined and processed. In addition to the grades of the ...
  • Machine learning in the mineral resource sector: An overview 

    Cevik, S. Ilkay; Ortiz, Julian M. (Queen's University, 2020)
    The increasing availability of the large and high-resolution geoscience data sets challenges the pattern recognition abilities of geoscientists. Machine learning algorithms provide opportunities to extend these pattern ...
  • Review of causal inference and modeling 

    Altinpinar, Mehmet; Ortiz, Julian M. (Queen's University, 2020)
    A cause and effect relationship is one of the universal constant realities and it can be observed in all aspects of life including physics, biology, medicine, psychology, engineering, management, law, statistics, economics ...
  • Investigating the application of random fields on manifolds in geostatistical modeling 

    Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2020)
    The twofold goal of this manuscript is first to briefly review some of the frameworks that allow the study and modeling of non-stationary random fields, and second, to notice that accounting for high-order dependencies in ...
  • Variograms of order w to measure departures from multiGaussianity 

    Casson, David; Ortiz, Julian M. (Queen's University, 2020)
    The study of geostatistics involves the inference and modelling of multivariate random functions. Frequently, this includes the assumption of multipoint Gaussianity (“multiGaussianity”) of the underlying multivariate random ...
  • An Unsupervised Clustering Approach for the Geostatistical Domaining of Univariate Data 

    Faraj, Fouad; Ortiz, Julian M. (Queen's University, 2020)
    The first formal step in geostatistical workflows consists of establishing domains and making the stationarity assumption for each domain. The validity of the stationarity assumption relies on samples within each domain ...
  • Integrating geometallurgical best practices in CIM definition standards guidelines 

    Garrido, Mauricio; Townley, Brian; Ortiz, Julian M.; Castro, Julio (Queen's University, 2020)
    Key geometallurgical responses and proxy variables need to be incorporated into the mineral resources and mining reserves estimation, to improve the performance of mining projects. The Canadian Institute of Mining, Metallurgy ...
  • Mineral processing modeling: a review 

    Moraga, Carlos; Kracht, Willy; Ortiz, Julian M. (Queen's University, 2020)
    Mineral processing comprises several stages including comminution, as crushing and grinding, and concentration through froth flotation. Complementary equipment is utilized to control particle size between stages. Models ...
  • Geometallurgical modeling of generic mineral processing plants 

    Moraga, Carlos; Kracht, Willy; Ortiz, Julian M. (Queen's University, 2020)
    A method to simulate mineral processing plants is developed for geometallurgical modeling. A modular simulation is implemented, thus each operation that comprises the process is programmed independently considering a ...
  • Introduction to sequential Gaussian simulation 

    Ortiz, Julian M. (Queen's University, 2020)
    Geostatistics deals with two different problems: estimation and uncertainty quantification. MultiGaussian and indicator kriging allow determining the local uncertainty at every location. However, these methods do not permit ...
  • Predictive Geometallurgy and Geostatistics Lab - Annual Report 2019 

    Avalos, Sebastian; Bolgkoranou, Maria; Cevik, S. Ilkay; Kracht, Willy; Midkiff, William; Olivo, Gema Ribeiro; Ortiz, Julian M.; Rielo, Oscar; Riquelme, Alvaro I. (Predictive Geometallurgy and Geostatistics Lab, 2019)
    The research developed during the last year is summarized in the Annual Report 2019. This report is made available to the general public for reference. Collaboration from other research groups and from industry is encouraged. ...
  • Machine Learning in Mineral Exploration: A Tutorial 

    Cevik, S. Ilkay; Ortiz, Julian M. (Queen's University, 2019)
    This tutorial aims to demonstrate how to conduct some machine learning methods in geoscience to enhance mineral exploration studies. The document does not intent to present an exhaustive list of all the methods available ...
  • Convolutional Neural Networks Architecture: A Tutorial 

    Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2019)
    Deep learning techniques have found an increasing number of applications in the field of geosciences. Among the most applied ones, Convolutional Neural Networks stand out by their ability to extract features from grid-like ...
  • A Simple, Synthetic and Two Dimensional Geometallurgical Modeling Application 

    Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2019)
    Geometallurgy seeks to provide a holistic framework across the mine value chain. Although it is possible to find real world applications in the literature, in most cases the integration is just between geological modeling ...

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