Abstract
Dark opaque constituents (mafic and heavy minerals, dark bioclastic fragments, disseminated organics) occur in varying amounts and combinations in beach and dune sands, with heavy-mineral concentrations (HMCs) punctuating quartz-dominated siliciclastic lithosomes. Typically occurring in trace amounts (<3-5%), heavy minerals provide important information about sediment source, regional transport pathways, and (paleo-)energy conditions, as well as produce strong reflections in georadar images when concentrated (>10%). Often comprising thin laminae (1–2 mm), HMCs present challenges for sampling, with traditional point-counts and heavy-mineral separation being extremely time-consuming. To address this issue, we present examples of simple grayscale-based field and micrograph analyses of samples from beach and coastal dune sites in the United States (Florida, New Jersey), the Baltic Sea (Estonia and Lithuania), and the Black Sea (Ukraine). Macrophotographs of exposures and trench walls show patterns of color intensity using image-processing freeware (e.g., Image J). Inverse grayscale (IG) values (white: IG=0; black: IG=255) along random profiles through outcrop or mounted grain photos help in readily distinguishing very dark minerals (magnetite, ilmenite, biotite, etc.; IG>240) from those with mid-range hues (feldspars, bioclastics, dark garnets; IG∼100–150) and near-clear translucent grains (fractured garnets, quartz; IG∼10–20), all on a white background. Once standardized for lighting, magnification, and granulometrically adjusted screen capture coverage, areal grayscale plots (histograms) aid in quantitative assessment of the relative “dark” fraction, with quartz-rich sand yielding low average color-intensity values (compared to even moderate HMCs (IG>60). Because denser mineralogical components often occur in the finer sand fraction, such analysis provides a conservative estimate of their relative contribution. Our approach has the potential to complement traditional sedimentological and geophysical (e.g., georadar) datasets with independent compositional metrics.