Abstract
Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier transform infrared spectroscopy (FTIR) and magnetic susceptibility (MS), and geochemical data from X-ray fluorescence (XRF; for total major oxides) and inductively coupled plasma-mass spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements) are performed on the survey's 268 topsoil samples (0-5 cm depth; 1 sample per km(2) ). Principal components (PCs) are calculated after imputation of censored data and centered log-ratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters ("heat maps") for the various analytical techniques. The sequential provenancing method is tested on the North Canberra soil survey with three "blind" samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR and MS (mineralogy), as well as XRF and total ICP-MS (geochemistry) analytical methods, offer the most precise and accurate provenance predictions. Inclusion of PCs in provenancing adds marginally to the performance. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications.