Multivariate modeling of glacimarine lithostratigraphy combining scanning XRF, multisensory core properties, and CT imagery: IODP Site U1419

Abstract

Marine sediments preserve archives of glacier behavior from many proxies, with lithofacies analysis providing direct evidence of glacial extent and dynamics. Many of these lithofacies have corresponding physical and geochemical properties that may be identified through quantitative, nondestructive logging properties. This study applies supervised and unsupervised classification to downcore logging data to attempt to model temperate glacimarine facies, which are independently identified via visual lithofacies analysis based on core photographs, digital X-radiography, and computed tomography scans. We test the limits of these methods by modeling both broad glacial and interglacial and small-scale variations in Late Pleistocene (< 60,000 yr) glacier extent leading into the Holocene deglaciation for a temperate ice stream at Integrated Ocean Drilling Program Site U1419 in the Gulf of Alaska. Multi-meter-scale mud and diamict lithofacies interpreted as non-glacial versus glacial conditions can be modeled with both methods using downcore physical property logging data (b* color reflectance, magnetic susceptibility, and natural gamma-ray activity) augmented with scanning X-ray fluorescence (XRF) elemental abundance (Ca, Zr, Si, K, Rb, and Al). Physical properties are most useful for delineating decimeter-meter-scale variations in composition and clay content, whereas scanning XRF elements best capture differences in sand versus clay content and composition at decimeter-centimeter scales. Neither classification technique can model the observed small-scale variations in diamict facies using elemental abundance from higher-resolution scanning XRF or from physical properties. Comparison of unsupervised cluster model results with observed lithofacies allows for identification of three different glacial conditions at Site U1419-ice-proximal, fluctuating, and retreating. For small-scale variations in glacial extent, cluster model results are best used as complementary data to image-based lithofacies identification rather than as a replacement.