Domaining by clustering multivariate geostatistical data

International audienceDomaining is very often a complex and time-consuming process in mining assessment. Apart from the further delineation of envelopes, a significant number of parameters (lithology, alteration, grades?) are to be combined in order to characterize domains or sub domains. This rapidly leads to a huge combinatory. 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 goal, 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, spectral methods and EM-based algorithms are reviewed. The methods are illustrated on mining data