This case study provides insight into magnetic susceptibility (MS), iron and bulk-sample geochemistry patterns in an Upper Mississippian siliciclastic–carbonate cyclothemic section Polotnyanyi Zavod (Moscow Basin, Russia), with many subaerial disconformities and Stigmaria impressions. The goal of this paper is to test whether the MS and geochemical signals in this section are linked to any specific geological processes. The section is dominated by limestones but contains several siliciclastic units and numerous subaerial disconformities. This lithological heterogeneity is vividly expressed on MS and bulk geochemical logs. MS shows the strongest positive correlation to bulk iron and also strong correlations to Al2O3, MgO, K2O and TiO2, pointing to close association of iron with siliciclastic fines rich in detrital mica and clays. The correlation of iron and MS to siliciclastic fines or subaerial exposure horizons is not straightforward. The highest ferruginization with most intense MS excursions occurs in basal sooty silts and shales of three main siliciclastic units of the studied section. In addition, many other thin pedogenized shales are ferruginized and show a relative high magnetism, but some ferruginized shales are not palaeosols.
Large datasets increasingly provide critical insights into crustal and surface processes on Earth. These data come in the form of published and contributed observations, which often include associated metadata. Even in the best-case scenario of a carefully curated dataset, it may be nontrivial to extract meaningful analyses from such compilations, and choices made with respect to filtering, resampling, and averaging can affect the resulting trends and any interpretation(s) thereof. As a result, a thorough understanding of how to digest, process, and analyze large data compilations is required. Here, we present a generalizable workflow developed using the Sedimentary Geochemistry and Paleoenvironments Project database. We demonstrate the effects of filtering and weighted resampling on Al 2 O 3 and U contents, two representative geochemical components of interest in sedimentary geochemistry (one major and one trace element, respectively). Through our analyses, we highlight several methodological challenges in a "bigger data" approach to Earth science. We suggest that, with slight modifications to our workflow, researchers can confidently use large collections of observations to gain new insights into processes that have shaped Earth's crustal and surface environments. 1 Supplemental Material: table of valid lithologies; map depicting sample locations; crossplot illustrating analytical uncertainty; flowchart of the proposed workflow; histograms showing the effects of progressive filtering, the distribution of spatial and age scales, and proximity and probability values; and results of sensitivity tests.
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