Levelling of multi-generational and spatially isolated geochemical surveys

Abstract

Large-scale, low-density geochemical surveys, aimed at chemically characterising the near surface and providing a range of geochemical environmental data, have been undertaken around the world. In Australia, the National Geochemical Survey of Australia (NGSA) covers ~81% of the continent with a lower sample density than most other continents, reflecting the remoteness of a large proportion of the continent and the associated cost of such programs. These factors make it highly unlikely that a high-density national survey will ever occur. Fortunately, a large number of spatially constrained higher-density surface sediment geochemical surveys have been undertaken in Australia over the last 50 years. Merging these survey data with the NGSA baseline presents an opportunity not only to provide a more detailed understanding of the chemistry of the Australian continent, but also to investigate issues related to sample density, e.g. scale-related chemical variance and reliability of the national low-density data. Merging these surveys however represents a challenge into how best to level the data in order to create a seamless dataset. We have investigated the possibility of merging such disparate datasets and show that this is feasible as long as: 1) sample media and grain size fraction are equivalent, and, 2) individual surveys are levelled to eliminate inter-laboratory differences. Levelling of geochemical data between surveys is a vital step and here we document a number of approaches we have utilised to undertake this. The results show that a variety of levelling methods (re-analysis, single standards, and multiple standards) can be used, with varying degrees of effectiveness, to create a seamless dataset, from spatially isolated surveys. We demonstrate the impact that such levelling can have on data using examples from the southern Thomson Orogen in southern Queensland and northern New South Wales, and the much larger survey from Cape York in north-eastern Australia. The levelled results show a significant improvement in the data allowing for more value to be extracted.