Multi-resolution graph-based clustering

Abstract

An apparatus and method for obtaining facies of geological formations for identifying mineral deposits is disclosed. Logging instruments are moved in a bore hole to produce log measurements at successive levels of the bore hole. The set of measurements at each such level of the bore hole interval is associated with reference sample points within a multidimensional space. The multidimensional scatter of sample points thus obtained is analyzed to determine a set of characteristic modes. The sample points associated with characteristic modes are grouped to identify clusters. A facies is designated for each of the clusters and a graphic representation of the succession of facies as a function of the depth is thus obtained. To identify the clusters, a “neighboring index” of each log measurement point in the data set is calculated. Next, small natural groups of points are formed based on the use of the neighboring index to determine a K-Nearest-Neighbor (KNN) attraction for each point. Independently of the natural group formation, an optimal number of clusters is calculated based on a Kernel Representative Index (KRI) and based on a user-specified resolution. Lastly, based on the data calculated from the prior steps, final clusters are formed by merging the smaller clusters.

Dependencies are inferred from the claim text.