From Outcrop to Spectrum—An Automated Approach to Modal Mineralogy of Silt‐Sized Sediment Applied to Central European Loess

Abstract

AbstractProvenance information from recent and ancient sedimentary archives is obscured by several factors and for disentangling these intermingled signals, analysis by multiple methods is paramount. In sedimentary provenance analysis (SPA), single‐grain methods determining mineralogy, chemical composition, or radiometric ages are of key importance but are mostly applied to sand‐sized sediments or sedimentary rocks. Finer grained sediments or sedimentary rocks are usually analyzed by whole‐rock geochemical means and seldom by single‐grain methods. Considering the abundance of fine‐grained sedimentary archives, a strong need for single‐grain, multi‐method analyses of silt‐sized sediments is obvious. Thus, we propose a workflow that is optimized for sample throughput and correlative analysis of fine‐grained sediments based on machine learning methods. The feasibility of the workflow is demonstrated by differentiating three Central European loess‐paleosol‐sequences. The increased sample throughput enables access to sedimentary archives at high spatial and/or temporal resolution, which will open up new research pathways in SPA of silt‐sized sediments.