Aeolian sediment fingerprinting using a Bayesian mixing model. Issue 14 (16th August 2017)

• Record Type: Journal Article
• Authors: Gholami, Hamid; Telfer, Matt W.; Blake, William H.; Fathabadi, Abolhassan
• Is Part Of: Earth surface processes and landforms. Volume 42:Issue 14(2017)
• Page Start: 2365
• Page End: 2376
• Publication Date: 2017-08-16
• Subjects: sand provenance -- aeolian sediment -- Markov chain Monte Carlo -- fingerprinting -- uncertainty; Geomorphology -- Periodicals; 551.4
• DOI: 10.1002/esp.4189
• Languages: English
• ISSNs: 0197-9337

Abstract

Abstract: Identifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed‐source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands. Eight samples were taken from dunes of the small (~58 km 2 ) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal–Wallis H‐tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, 87 Sr, (La/Yb)n, Ga and δCe), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework. There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed‐source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U–Pb dating) are not possible due to mineralogical constraints. Copyright © 2017 John Wiley & Sons, Ltd. Abstract : An understanding of sediment transport pathways in aeolian landscapes is important for land management, and can elucidate long‐term landscape evolution. The sophistication of provenance methods for aeolian sediments has lagged behind those of fluvial science, and thus here we demonstrate a Bayesian Markov Chain Monte Carlo (MCMC) method of quantitative fingerprinting of aeolian sands, and providing accompanying uncertainties on those estimates. The results suggest the method may be widely applicable in identifying aeolian sand sources.