Domaining by Clustering Multivariate Geostatistical Data

  • Thomas Romary
  • Jacques Rivoirard
  • Jacques Deraisme
  • Cristian Quinones
  • Xavier Freulon
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 17)


Domaining is very often a complex and time-consuming process in mining assessment. Apart from the delineation of envelopes, a significant number of parameters (lithology, alteration, grades) are to be combined in order to characterize domains or subdomains within the envelopes. This rapidly leads to a huge combinatorial problem. Hopefully the number of domains should be limited, while ensuring their connectivity as well as the stationarity of the variables within each domain. In order to achieve this, different methods for the spatial clustering of multivariate data are explored and compared. A particular emphasis is placed on the ways to modify existing procedures of clustering in non spatial settings to enforce the spatial connectivity of the resulting clusters. K-means, hierarchical methods and model based algorithms are reviewed. The methods are illustrated on a simple example and on mining data.


Delaunay Triangulation Markov Random Field Dissimilarity Matrix Hierarchical Cluster Algorithm Hierarchical Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Thomas Romary
    • 1
  • Jacques Rivoirard
    • 1
  • Jacques Deraisme
    • 2
  • Cristian Quinones
    • 3
  • Xavier Freulon
    • 3
  1. 1.Geosciences/Geostatistics CenterMines ParisTechFontainebleauFrance
  2. 2.GéovariancesAvon CedexFrance
  3. 3.Mining Business Unit, Direction Strategy—Resources and ReservesAREVA NCParis La Défense CedexFrance

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