93276 - "Mining Geostatistics M"

Learning outcomes

The course aims to provide the elements needed to characterize, model and map mineral and energy georesources. The learning outcomes can be applied to various aspects of the extractive sector: exploration, excavation, processing, post-closure and environmental rehabilitation.

Course contents

Students will learn the theoretical and practical tools to spatially characterize the minerals' grades, for the 3D reconstruction of the deposit and for the estimation and quantification of the available resources and reserves. Results incorportate a probabilistic component and are identified in terms of risk, even economic.

In the following, the main sectors of mining engineering where geostatistics is applied are listed:

In the exploration phase:

• The optimization of sampling;
• The definition and selection of rich resources of raw materials to be exploited.

In the excavation phase:

• The management of stockpiles;
• The control of plant feeding for mineral treatment.

In the environmental rehabilitation phase, together with the mining activity and at the post-closure stage:

• The definition of the polluted area for their recovery;
• The cartography of the spatial and temporal distributions of substances in the mining residues for their potential future recovery;
  

Reccommended knowledge

Statistics, probability, geomatics, cartography, mining engineering.

Course contents

Module 1

• Basic review on Probability and Statistics
• Regionalized Variables
• Experimental Variogram and Models
  
• Recall on traditional estimators
• Ordinary and Simple Kriging
  

Module 2

• Data Regularization and Dispersion
• Support and Information Effect on Predictions
  
• Multivariate Geostatistics
• Basics of Non-Stationary Geostatistics
• Cross validation

The course is completed with a practical project on a mineral georesource, with the 3D reconstruction of the deposit and technical and economic analyses about the potential areas for successful exploitation.

Readings/Bibliography

• Armstrong M.; Basic Linear Geostatistics, Springer Berlin, Heidelberg, 1998
• Bruno, R.; Raspa G. La pratica della geostatistica lineare: il trattamento dei dati spaziali Guarini Studio, 1994
• Chiles, J.P.; Delfiner, P. Geostatistics Modeling Spatial Uncertainty, 2nd ed.; WILEY: Hoboken, NJ, USA, 2012
• Emery, X.; Séguret S.A. Geostatistics for the Mining Industry, CRC Press, Taylor & Francis Group, 2023
• Journel A.G.; Huijbregts Ch.J. Mining Geostatistics, Blackburn Press, 2003
• Matheron, G. The Theory of Regionalized Variables and Its Application; École Nationale Supérieure des Mines de
  Paris: Paris, France, 1971
• Remy, N.; Boucer, A. and Wu J. Applied Geostatistics with SGeMS, Cambridget University press 2009

Teaching methods

The course is made by lectures, with the support of dedicated presentations and real applications.

Specific exercises, also with basics of programming, will train students to develop geostatistical methodologies to solve practical problems.

Moreover, some time will be dedicated to practical training over open source softwares, basically tools for managing large datasets and conducting geostatistical analyses over them.

Assessment methods

The exam will be divided in two parts:

1. Written part, with questions over the theory and the materials presented during the course (1 hour). Half of the score
   
2. Oral part, based on the discussion over one practical application. (1/2 hour). Half of the score.

The final score will consist half of result of the written exam and half of the oral discussion over the project.

Teaching tools

Lessons are based on powerpoint presentations supported by blackboard.

The exercises will be carried out at the Didactic-informatic Laboratory. Practical applications will be conducted with the support of basic programming tools and open source softwares.

Office hours

See the website of Sara Kasmaeeyazdi

See the website of Francesco Tinti