Assessing equivalence between geochemical datasets and leveling data when mapping an area covered by multiple geochemical datasets

In many parts of the world, multiple geochemical datasets are available for cartographers when mapping a region. Using multiple datasets for regional geochemical mapping can be highly beneficial for establishing geochemical... [ view full abstract ]

In many parts of the world, multiple geochemical datasets are available for cartographers when mapping a region. Using multiple datasets for regional geochemical mapping can be highly beneficial for establishing geochemical maps with increased resolution and/or coverage area. This practice involves assessing the equivalence between datasets and, if needed, applying data leveling to correct possible biases between datasets. Here we present two original methods for assessing equivalence and for leveling data when datasets contain records that are located within the same perimeter. The first method is designed for datasets similarly spatially distributed and is based on the Kolmogorov-Smirnov test and quantile regression. The second method does not require datasets to be similarly spatially distributed and is based on the prior knowledge about the factors explaining the geochemical concentrations and on Bivariate Least Squares regression. The scope of application, pros, cons and detailed practical recommendations are presented for each method. Both methods were then applied to a case study involving Fe, V and Y datasets originating from two European geochemical mapping projects: the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) and the Baltic Soil Survey (BSS). The two methods yielded similar results thereby suggesting that they are both reliable and effective enough to take advantage of the steadily increasing number of geochemical datasets available for mapping a region.