X-ray fluorescence scanning of soft and wet-sediment cores in terrestrial environments : A robust blind source separation approach

Abstract

X-ray fluorescence (XRF) scanning of split cores using automated cores scanners is rapid but expensive and semi-quantitative method in sedimentary geochemistry. While searching for a low-cost and quantitative solution in environmental geochemistry, we investigated the application of hand-held XRF device in scanning on two siliciclastic-sediment cores from dam-reservoirs in Slovakia. The core-scan data were compared with XRF data from dry, powdered sample aliquots, grain size, water content, and magnetic susceptibility data, which were used as independent variables. The aim of the manuscript is to investigate the applicability of time-series statistical methods to reveal the physical and chemical signals from hand-held XRF scanning of wet-sediment cores. The geochemical data were processed using the centred log-ratio (clr) technique, and then by the Second-Order Blind Identification (SOBI) method, which is a subtype of robust Blind Source Separation (BSS) techniques commonly used to enhance the signal-to-noise ratio in time series analysis of data in medicine or acoustics. So far, BSS has been rarely applied in geochemistry. Raw element concentrations (ppm/wt%) of core scans and sediment powders are essentially incomparable, but their clr coefficients show statistically significant correlation for Al, P, K, Ca, Mn, Fe, Cu, As, Sr, Zr, and Pb, which reduces the difference between the two measurement methods and justifies previous attempts to calibrate the core-scanning data using log-ratio techniques. Scores of three of eight latent components (IC2, IC4, and IC7) found by SOBI in the core scan- and powder data show significant statistical correlation demonstrating the applicability of the core-scan technique. Their geochemical interpretation is based on the IC loadings by element clr, and the stratigraphic correlation with the sediment grain size and water content. These ICs explain geochemical variability due to grain-size contrast between sandy and silty layers, input of biogenic productivity-sensitive elements (Ca and P), eutrophication, and accumulation of anthropogenic elements. The robust BSS approach represents a promising method of processing of large datasets in environmental geochemistry.