Browsing The Robert M. Buchan Department of Mining Faculty Publications by Title

Now showing items 1-20 of 31

    • 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 ...

    • Exploring the RCNN Technique in a Multiple-Point Statistics Framework 

      Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2019)
      This work explores the Recursive Convolutional Neural Network (RCNN) technique in terms of describing (1) the inner architecture, (2) the training process and (3) the simulation algorithm in a multiple-point statistics ...

    • A General Approach to the Assessment of Uncertainty in Volumes by Using the Multi-Gaussian Model 

      Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2019)
      The goal of this research is to derive an approach to assess uncertainty in an arbitrary volume conditioned by sampling data, without using geostatistical simulation. We have accomplished this goal by deriving an numerical ...

    • Geometallurgical Modeling Framework 

      Ortiz, Julian M. (Queen's University, 2019)
      Geometallurgical modeling allows understanding and transferring the variability of input variables into the downstream processes. In this paper, we present the overview of the geometallurgical modeling framework, and show ...

    • 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 ...

    • 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 ...

    • 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 ...

    • 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 ...

    • 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 ...

    • Knowledge Discovery from Geochemical Data with Supervised and Unsupervised Methods 

      Cevik, S. Ilkay; Olivo, Gema Ribeiro; Ortiz, Julian M. (Queen's University, 2019)
      As mineral exploration activities tend to aim at deeper targets, costs per discoveries are getting higher. Therefore, the effective utilization of all the available data is critical in the decision-making process. In recent ...

    • A Literature Review on P-wave Velocities in Rock Under Compression 

      Midkiff, William; Rielo, Oscar; Ortiz, Julian M. (Queen's University, 2019)
      P-waves are acoustic waves propagated through geological mediums. P-wave velocities are increasingly being used in natural resource industries. The purpose of this paper is to present a literature background on the behavior ...

    • 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 ...

    • 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 ...

    • A mechanism and thermodynamic model for the interaction of silicate minerals with lead-assisted gold electro-oxidation in a dilute cyanide medium 

      Kim, Rina; Ghahreman, Ahmad (Elsevier BV, 2019-07-22)
      Gold (Au) oxidation kinetics in cyanide (CN) solutions can be increased by the addition of low concentrations of aqueous lead [Pb(II)]. However, the Pb(II) addition is not always effective in improving Au oxidation kinetics. ...

    • 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 ...

    • Modeling the Uncertainty in Geologic Volumes: The Log-Normal Case 

      Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2019)
      The aim of the study is to develop a methodology that allows the quantification of the uncertainty in an arbitrary volume conditioned by sampling data, without the use of the traditional geostatistical simulation, which ...

    • MultiGaussian Kriging: A Review 

      Ortiz, Julian M. (Queen's University, 2019)
      The kriging estimate has an associated kriging variance which measures the variance of the error between the estimate and the true value. However, this measure of uncertainty does not depend on the actual value of the ...

    • 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. ...

    • Multivariate Geostatistical Simulation using Principal Component Analysis 

      Bolgkoranou, Maria; Ortiz, Julian M. (Queen's University, 2019)
      Multivariate geostatistical simulation is aimed at reproducing the statistical relationships between variables and their spatial distribution. We present a methodology whereby grades and a filler variable are transformed ...

    • 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. ...