Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

© 2016 Elsevier Ltd This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges- Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns

Modelling particle-size distributions from operator estimates of sediment particle-size

Estimates of particle-size made by operators in the field and laboratory represent a vast and relatively untapped data archive. The wide spatial distribution of particle-size estimates makes them ideal for constructing geological models and soil maps. This study uses a large data set from the Netherlands (n = 4837) containing both operator estimates of particle-size and complete particle-size distributions measured by laser granulometry. Operator estimates are inaccurate and imprecise relative to measured laser data; only 16.68% of samples were successfully classified using the Dutch classification scheme for fine sediment. Operator estimates of sediment particle-size encompass the same range of percentage values as particle-size distributions measured by laser analysis. However, the distributions measured by laser analysis show that most of the sand percentage values lie between 0 and 1, so the majority of the variability in the data is lost because operator estimates are made to the nearest 1% at best, and more frequently to the nearest 5%. Operator estimates made by three technicians trained by the Geological Survey of the Netherlands are found not to be influenced by bias, rather they exhibit very similar levels of accuracy and precision. This study compares five different methods of modelling complete particle-size distributions from sparse data: (i) a four-part Pearson's probability distribution function, (i) a log-linear interpolation, (iii) a logit-linear interpolation, (iv) a logistic probability distribution function and (v) a logit constrained cubic-spline (logit-CCS) interpolation. The logit-CCS interpolation performed best across all the samples used, although the performance of all models was very similar for normal Gaussian, skewed and peaked distributions. Predictions for bimodal distributions using the Pearson's, logit-linear and logistic models are markedly less accurate than both log-linear and logit-CCS interpolation models. Although the logit-CCS interpolation model produces the best predictions of continuous particle-size distributions, the low accuracy and precision of operator estimates does not warrant the use of such a complex algorithm. Given this, it is suggested that a standard log-linear interpolation is the most effective means of modelling complete particle-size distributions from sparse data. Interpolation-based models are recommended over probability distribution functions because they allow for a greater degree of flexibility and will always honour the available input data

Muddled or mixed? Inferring paleoclimate from size distributions of deep-sea clastics

status: publishe

Genetically meaningful decomposition of grain-size distributions.

status: publishe

Reservoir Modelling of Lower Cretaceous West Netherland Basin Aquifers for Geothermal Energy Production

No abstract available

3D modelling of particle-size distributions in the shallow subsurface: Zeeland, The Netherlands

Geoscience & EngineeringCivil Engineering and Geoscience

Controls on terrigenous sediment supply to the Arabian Sea during the late Quaternary: the Makran continental slope.

The input of terrigenous sediment along the tectonically active Makran continental margin off south-western Pakistan (Gulf of Oman, northern Arabian Sea) is studied on the basis of sediment cores distributed along a transect from the upper slope to the abyssal plain. Spatial and temporal variations in sediment composition, sedimentation rate and turbidite frequency in late Pleistocene-Holocene time (last ~2

Reservoir Modelling of Lower Cretaceous West Netherland Basin Aquifers for Geothermal Energy Production

No abstract available