Creating solutions for the mining industry


Our key competences.

Research + Technology

We combine the expertise of our multidisciplinary team to push forward the mining industry with innovative and fresh ideas.


Modelling in mining is about Big Data. We successfully tackle this by fine tuning algorithms to run on small and powerful hardware.


We have pioneered in the region the technology transfer from the university to our industrial partners.


Some of our most recent work


Alges Library for Creative Image Analysis


Geostatistical Software Library


Software for geometric restitution of geological ore bodies


These are our latest publications.

  1. Paravarzar, S., Emery, X., & Madani, N. (2015). Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits. Comptes Rendus Geoscience, 347(2), 84–93.
  2. Tajvidi, E., Monjezi, M., Asghari, O., Emery, X., & Foroughi, S. (2015). Application of joint conditional simulation to uncertainty quantification and resource classification. Arabian Journal of Geosciences, 8(1), 455–463.
  3. Maleki, M., & Emery, X. (2015). Joint Simulation of Grade and Rock Type in a Stratabound Copper Deposit. Mathematical Geosciences, 47(4), 471–495.
  4. Talebi, H., Asghari, O., & Emery, X. (2015). Stochastic rock type modeling in a porphyry copper deposit and its application to copper grade evaluation. Journal of Geochemical Exploration, 157, 162–168.
  5. Cárdenas, E., Townley, B., Ortiz, J., & Kracht, W. (2014). Geochemical quantitative mineral characterization for geometallurgical applications in porphyry copper deposits.
  6. Ortiz, J. M. (2014). Geometallurgical Modelling and Mine Planning, CSIRO Chile Program 2.
  7. Ortiz, J. M., & Magri, E. J. (2014). Designing and Advanced RC Drilling Grid for Short-Term Planning in Open Pit Mines: Three Case Studies. The Journal of the Southern African Institute of Mining and Metallurgy, 114(8), 631–639.
  8. Pérez, C., Mariethoz, G., & Ortiz, J. M. (2014). Verifying the high-order consistency of training images with data for multiple-point geostatistics. Computers & Geosciences, 70, 190–205.
  9. Peredo, O., & Ortiz, J. M. (2014). Resurrecting GSLIB by code optimization and multi-core programming. In Geostatistical and geospatial approaches for the characterization of natural resources in the environment: Challenges, Processes and Strategies. New Dehli, India.
  10. Peredo, O., Ortiz, J. M., Herrero, J. R., & Samaniego, C. (2014). Tuning and Hybrid Parallelization of a Genetic-based Multi-Point Statistics Simulation Code. Parallel Computing, 40(5), 144–158.
  11. Peredo, O., Ortiz, J. M., & Leuangthong, O. (2014). Inverse modeling of moving average kernels for 3D Gaussian simulation. Paris, France.
  12. Rezaee, H., Asghari, O., Koneshloo, M., & Ortiz, J. M. (2014). Multiple-Point Geostatistical Simulation of Dykes: Application at Sungun Porphyry Copper System, Iran. Stochastic Environmental Research and Risk Assessment, 1–15.
  13. Emery, X., & Lantuéjoul, C. (2014). Can a training image be a substitute for a random field model? Mathematical Geosciences, 46(2), 133–147.
  14. Arroyo, D., & Emery, X. (2014). Simulation of Intrinsic Random Fields of Order k with Gaussian Generalized Increments by Gibbs Sampling. Mathematical Geosciences, 1–20.
  15. Emery, X., Arroyo, D., & Peláez Marı́a. (2014). Simulating large Gaussian random vectors subject to inequality constraints by Gibbs sampling. Mathematical Geosciences, 46(3), 265–283.

Meet Our Team

The people who make our solutions work for you.

Antonio Barberán


Álvaro Egaña

Executive Director

Carlos González


Cristóbal Silva


Daniel Baeza


Felipe Navarro


Fabian Soto


Mauricio Garrido


Marcia Ojeda

Master Student

Xavier Emery

Academic Director

Industrial & Scientific Partners

The people we work with.