93276 - GEOSTATISTICA MINERARIA 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

The importance of raw material resources is increasing in the daily-life and it leads to a comprehensive understanding of their values. The characterization of mineral raw materials and the related environmental aspects involves various mining activities. 

Specifically, they can be distinguished:

During the exploration:

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

During the excavation:

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

During the environmental rehabilitation, even at 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;
  

All the mentioned actions can be quantified by data collection and integration, statistical and spatial analysis of selected data (samples) and predicting data at points where no data is available based on mathematical models. Geostatistical approaches help to characterize the spatial, temporal and space-time variability to reach a 3-dimension model or/and 2- dimension map, together with the uncertainty of the predicted values. The correct analysis allows to optimize the results in economic terms.

Course contents

The basic knowledge of statistics, probability, geomatics, cartography and mining engineering are highly reccommended.

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

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 open source softwares, to give students tools for managing large datasets.

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 and will propose practical applications with the support of basic programming tools and open source softwares.

Office hours

See the website of Francesco Tinti

See the website of Sara Kasmaeeyazdi