Author Archives: Julian Ortiz Cabrera

[Aug 2019] Sequential Indicator Simulation with Locally Varying Anisotropy – Simulating Mineralized Units in a Porphyry Copper Deposit.

This paper was published in a new research journal supported by the Instituto de Ingenieros de Minas de Chile (IIMCh). This is work done by the Master student Rodrigo Gutierrez, while Dr. Ortiz was at Universidad de Chile. The paper can be downloaded here.


Sequential Indicator Simulation with Locally Varying Anisotropy – Simulating Mineralized Units in a Porphyry Copper Deposit.

R. Gutierrez (Department of Mining Engineering, Universidad de Chile, Avda. Tupper 2069, Santiago 837 0451, Chile), J. M. Ortiz (The Robert M. Buchan Department of Mining, Queen’s University, 25 Union Street, Kingston, ON K7L 3N6, Canada)

ABSTRACT: The definition of mineralized units is key to the predictive capacity of a resource model, since these units define homogeneous volumes where spatial estimation or simulation of the relevant grades can be performed. In this paper, we adapt sequential indicator simulation to model mineralization units in a large porphyry copper deposit, accounting for the weathering profile that defines the vertical zoning of these units. A locally varying anisotropy field is created from the geological interpretation of the contact between the mixed mineralized unit, where the rock mineralization transitions from supergene to hypogene sulphides. A sequential indicator simulation routine is modified to account for the local variations of the units, and all distances are computed through these folded surfaces. Sensitivities related to the main parameters of the simulation algorithm that accounts for the locally varying anisotropy are performed to select the optimum parameters. The final result is compared with conventional sequential indicator simulation, against the geological units logged in blast holes, at a much denser grid, showing an increase in the accuracy in predicting the mineralized unit from the drillhole logged data.

[Jun 2019] Presenting at APCOM 2019 in Wroclaw, Poland

Julian Ortiz, Maria Bolgkoranou and Sebastian Avalos attending APCOM 2019.

During APCOM 2019, our group presented three papers:

  • Bolgkoranou M, Ortiz JM (2019) Multivariate geostatistical simulation of compositional data using Principal Component Analysis – Application to a Nickel laterite deposit.
  • Nelis G, Ortiz JM, Morales N (2019) Performance assessment of antithetic random fields in a stochastic mine planning model.
  • Avalos S, Ortiz JM (2019) Recursive Convolutional Neural Networks in a Multiple-Points Statistics Framework.

[Mar 2019] Hardness variability impact on sizing a solar energy system integrated into a SAG mill

Two papers have been published in collaboration with colleagues from Universidad de Chile and University of Stuttgart in the journal Minerals Engineering.

Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part I: Framework, model development and effect of solar irradiance forecasting

Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part II: Effect of ore hardness variability, geometallurgical modeling and demand side management

The papers can be donwloaded here (part I) and here (part II).


[Mar 2019] İlkay Çevik presented a poster at PDAC – SEG Student Minerals Colloquium: “Random forest classification and PCA in geochemical data”.

Ilkay was at PDAC in Toronto, presenting his work in collaboration with Nexa Resources, on the application of Random Forest and PCA to support geological interpretation of geochemical data. This work is done in collaboration with Dr. Gema Olivo from the Department of Geological Sciences and Geological Engineering, at Queen’s University.

You can download the poster below.

[Jan 2019] Sampling Strategies for Uncertainty Reduction in Categorical Random Fields: Formulation, Mathematical Analysis and Application to Multiple-Point Simulations

New paper in Mathematical Geosciences to start 2019, showcasing the work of Felipe Santibanez, Ph. D. student at Universidad de Chile, in collaboration with Prof. Jorge Silva. You can download it here.


Sampling Strategies for Uncertainty Reduction in Categorical Random Fields: Formulation, Mathematical Analysis and Application to Multiple-Point Simulations

Felipe Santibañez (Information and Decision System Group (IDS), Department of Electrical Engineering, University of Chile), Jorge F. Silva (Information and Decision System Group (IDS), Department of Electrical Engineering, University of Chile), Julian M. Ortiz (The Robert M. Buchan Department of Mining, Queen’s University)

Abstract

The task of optimal sampling for the statistical simulation of a discrete random
field is addressed from the perspective of minimizing the posterior uncertainty of non-sensed positions given the information of the sensed positions. In particular, information theoretic measures are adopted to formalize the problem of optimal sampling design for field characterization, where concepts such as information of the measurements, average posterior uncertainty, and the resolvability of the field are introduced. The use of the entropy and related information measures are justified by connecting the task of simulation with a source coding problem, where it is well known that entropy offers a fundamental performance limit. On the application, a one-dimensional Markov chain model is explored where the statistics of the random object are known, and then the more relevant case of multiple-point simulations of channelized facies fields is studied, adopting in this case a training image to infer the statistics of a nonparametric model. In both contexts, the superiority of information-driven sampling strategies is proved in different settings and conditions, with respect to random or regular sampling.

[Nov 2018] Change of support using non-additive variables with Gibbs Sampler: Application to metallurgical recovery of sulphide ores

Another paper published in Computers & Geosciences. You can download it here.


Change of support using non-additive variables with Gibbs Sampler: Application to metallurgical recovery of sulphide ores

Mauricio Garrido (ALGES Laboratory, AMTC, Universidad de Chile, Chile), Julian M. Ortiz (The Robert M. Buchan Department of Mining, Queen’s University, Canada), Francisco Villaseca (Codemining Consultant, Chile), Willy Kracht (Advanced Mining Technology Center (AMTC) and Department of Mining Engineering, Universidad de Chile, Chile), Brian Townley (ALGES Laboratory, AMTC and Department of Geology, Universidad de Chile, Chile), Roberto Miranda (Codemining Consultant, Chile)

Abstract

Flotation tests at laboratory scale describe the metallurgical behavior of the minerals that will be processed in the operational plant. This material is generally composed of ore and gangue minerals. These tests are usually scarce, expensive and sampled in large supports. This research proposes a methodology for the geostatistical modelling of metallurgical recovery, covering the change of support problems through additive auxiliary variables. The methodology consists of simulating these auxiliary variables using a Gibbs Sampler in order to infer the behavior of samples with smaller supports. This allows downscaling a large sample measurement into smaller ones, reproducing the variability at different scales considering the physical restrictions of additivity balance of the metallurgical recovery process. As a consequence, it is possible to apply conventional multivariate geostatistical tools to data at different supports, such as multivariable exploratory analysis, calculation of cross-variograms, multivariate estimations, among others. The methodology was tested using a drillhole database from an ore deposit, modelling recovery at a smaller support than that of the metallurgical tests. The support allowed for the use of the geochemical database, to consistently model the metal content in the feed and in the concentrate, in order to obtain a valid recovery model. Results show that downscaling the composite size reduces smoothing in the final model.