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Ilmenite is one of the common kimberlitic indicator minerals recovered during diamond exploration, and its distinction from non-kimberlitic rock types is important. This is particularly true for regions where these minerals are present in relatively low abundance, and they are the dominant kimberlitic indicator mineral recovered. Difficulty in visually differentiating kimberlitic from non-kimberlitic ilmenite in exploration concentrates is also an issue, and distinguishing kimberlitic ilmenite from those derive from other similar rocks, such as ultramafic lamprophyres, is practically impossible. Ilmenite is also the indicator mineral whose compositional variety has the most potential to resolve provenance issues related to mineral dispersions with contributions from multiple kimberlite sources. Various published data sets from selected kimberlitic (including kimberlites, lamproites, and various ultramafic lamprophyres) and non-kimberlitic rock types have been compiled and evaluated in terms of their major element compositions. Compositional fields and bounding reference lines for ilmenites derived from kimberlites (sensu stricto), ultramafic lamprophyres, and other non-kimberlitic rock types have been defined primarily on MgO–TiO2 graphs as well as MgO–Cr2O3 relationships.

Regional and local structural controls on the emplacement of 1326 Southern African kimberlites and related rocks (kimberlites sensu lato, 11% of which are dated) are analysed using a framework of lineaments defined by combining geology, aeromagnetics, gravity and geomorphological data. Spatial analysis of occurrences within clusters of kimberlites less than 100km across resolves variable trends, depending on the age and position of the cluster; but on a regional scale the distribution of these clusters is statistically controlled by four lineament trends: 040°, 096°, 134° and 165°. Similar regional trends are observed as aspect lineaments that can be followed over large distances from modelling the variation in dip direction of the Southern African topography. These observations suggest that different geological parameters exert a control on the distribution of kimberlites. Local structures may include en-echelon fault arrays, Riedel, R’-, P- or T-structures within trans-continental lithosphere structures (cryptic continental corridors). Many cryptic continental corridors are collinear with fracture zones along the Atlantic and Indian continental margins of Southern Africa, and may have found their origin in events resulting from plate reorganization during the break-up of the supercontinent Gondwana. Fault resistance may have rapidly changed the stress state of the African continent causing the deep lithospheric faults to be the loci of episodic extension, allowing kimberlite fluids to ascend through the faults and cluster within near-surface structures. A progressive age variation of kimberlite magmatism in Southern Africa may be attributed to stress propagation along deep lithospheric fractures.

A geostatistical analysis was made on the areal distribution pattern of extracted heavy metals in surface sediments of the Skagerrak and Kattegat. Normalisation was done on heavy metal concentrations with respect to content of organic carbon at 112 stations. Kriging has been applied to determine the spatial distribution pattern of contaminated suspension load. The statistical treatment of the data included variogram analysis and cross-validation in order to reveal the spatial variability over the study area. The variograms clearly indicate spatial dependency and it is demonstrated that the statistical range of correlation is comparatively large. This implies that even with a wide-spaced sampling grid, a geostatistical approach is applicable in the marine environment. Results presented in the form of gridded maps, in association with kriging standard deviations, describe the spreading effects over a comparatively large marine area. A geostatistical approach using kriging shows to be an effective t...