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

Flood, Rory; Bloemsma, M.R.; Weltje, Gert Jan; Barr, Iestyn D.; O’Rourke, Sharon; Turner, Jonathan; Orford, Julian D.

doi:10.1016/j.apgeochem.2016.06.006

Cited by 29 publications

(19 citation statements)

References 28 publications

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“…Weltje & Tjallingii () introduced a method for calibration of XRF‐core‐scanners using log‐ratio calibration equations (LRCEs) based on cross‐correlation of scanner log‐ratio intensities and log‐ratios of independently measured element concentrations. The LRCE‐based calibration can significantly reduce the effects of overlap between elements and problems due to closure inherent in compositional datasets, particularly when there is variation in an element that cannot be measured by the scanner (Weltje & Tjallingii, ; Flood et al ., ). The downside of this approach is that interpretation of log ratio trends is less intuitive.…”

Section: Dataset and Methodsologymentioning

confidence: 97%

“…Consequently, the Ross Formation cores do not seem to suffer from some of the calibration issues observed elsewhere in soft‐sediment and water‐saturated cores (e.g. Flood et al ., ). This likely reflects the relatively simple quartzose composition, minimal porosity (<1%), smooth cut core surfaces and the absence of interstitial pore fluids.…”

Section: Dataset and Methodsologymentioning

confidence: 97%

“…The fan system advanced into a poorly oxygenated basin so the event beds are unbioturbated and hence retain their original depositional character. Helium porosity measurements on Ross Formation core plugs are low ( ca 0.5%; Pierce et al ., ) as a result of early cementation and moderate compaction, implying that there are no issues with variable porosity and interstitial fluids that complicate XRF scanner interpretation of soft‐sediment cores (MacLachlan et al ., ; Flood et al ., ).…”

Section: Geological Backgroundmentioning

confidence: 97%

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High‐resolution X‐ray fluorescence profiling of hybrid event beds: Implications for sediment gravity flow behaviour and deposit structure

et al. 2020

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Hybrid event beds form when turbidity currents that transport or locally acquire significant quantities of mud decelerate. The mud dampens turbulence driving flow transformations, allowing both mud and sand to settle into dense, near‐bed fluid layers and debris flows. Quantifying details of the mud distribution vertically in what are often complex tiered deposits is critical to reconstructing flow processes and explaining the diverse bed types left by mud‐bearing gravity flows. High‐resolution X‐ray fluorescence core scanning provides continuous vertical compositional profiles that can help to constrain mud distribution at sub‐millimetre scale, offering a significant improvement over discrete sampling. The approach is applied here to cores acquired from the Pennsylvanian Ross Sandstone Formation, western Ireland, where a range of hybrid event beds have been identified. Raw X‐ray fluorescence counts are calibrated against element concentrations and mineral abundances determined on coincident core plugs, with element and element log‐ratios used as proxies to track vertical changes in abundances of quartz, illite (including mica), chlorite and calcite cement. New insights include ‘stepped’ (to higher values) as opposed to ‘saw‐tooth’ vertical changes in mud content and the presence of compositional banding that would otherwise be overlooked. Hybrid event beds in basin floor sheets that arrived ahead of the prograding fan system have significantly cleaner sandy components than those in mid‐fan lobes. The latter may imply that the heads of the currents emerging from mid‐fan channels entrained significant mud immediately before they collapsed. Many of the H3 debrites are bipartite with a sandier H3a division attributed to re‐entrainment and mixing of a trailing debris or fluid mud flow (H3b) with sand left by the forward part of the flow. Hybrid event bed structure may thus partly reflect substrate interaction and mixing during deposition, and the texture of the bed divisions may not simply mirror those in the suspensions from which they formed.

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Section: Dataset and Methodsologymentioning

confidence: 97%

Section: Dataset and Methodsologymentioning

confidence: 97%

Section: Geological Backgroundmentioning

confidence: 97%

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High‐resolution X‐ray fluorescence profiling of hybrid event beds: Implications for sediment gravity flow behaviour and deposit structure

et al. 2020

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“…PLS regression is a multilinear technique that establishes a basis explaining the maximum covariance between two sets of variables. It is particularly suitable for high‐dimensional data sets that have multicollinearity among variables (Bloemsma et al, ; Flood et al, ; Geladi & Kowalski, ; Wold et al, ). Each model is represented by a predictive function in form of

italicGrain italicsize ((), φ) = E + β_{1} \cdot X_{1} + β_{2} \cdot X_{2} + \dots + β_{n} \cdot X_{n},

where E equals the intercept, β n is the weight (regression coefficient) of each element, and X n represents the clr‐transformed geochemical data.…”

Section: Methodsmentioning

confidence: 99%

“…PLS regression is a multilinear technique that establishes a basis explaining the maximum covariance between two sets of variables. It is particularly suitable for high-dimensional data sets that have multicollinearity among variables (Bloemsma et al, 2012;Flood et al, 2016;Geladi & Kowalski, 1986;Wold et al, 2001). Each model is represented by a predictive function in form of…”

Section: Pls Regressionmentioning

confidence: 99%

An Empirical Method to Predict Sediment Grain Size From Inorganic Geochemical Measurements

Liu

Bertrand

Weltje

2019

Geochem Geophys Geosyst

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Grain size is one of the most fundamental properties of sediments. It is frequently used in paleoclimate, paleoceanographic, and paleoenvironmental research as a proxy for river discharge, current and wind strength, and to identify mass flow deposits. Measuring grain size is, however, time‐consuming and destructive. Given the strong influence of grain size on sediment inorganic geochemistry, single‐element variations measured by, for example, X‐ray fluorescence core scanning are increasingly used to estimate grain‐size variations at high resolution in sediment cores. This approach is however limited to a narrow grain‐size range since individual elements only monotonically relate to grain size over a narrow size range. Here, we present a simple, code‐free, multielement method based on partial least squares regression to predict sediment mean grain size from inorganic geochemical data over the range of sizes commonly encountered in sedimentary basins (clay to sand). The method was first tested using river sediment samples separated in 11 grain‐size fractions, and it was later successfully applied to two sediment cores from the Chilean fjords. Our method only requires measuring grain size on a limited number (around 10) of selected training samples, and it allows to predict mean grain size at X‐ray fluorescence core scanner resolution. This method has the potential to be applied to any lake or marine sediment core, provided sediment provenance, weathering, and diagenesis remain relatively stable through time, and we anticipate that it will result in a significant increase in the resolution of sediment proxy records of climate and environmental change.

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The Indian Sundarbans: Biogeochemical Dynamics and Anthropogenic Impacts

Henderson

Das

Ghosh

et al. 2021

Estuarine Biogeochemical Dynamics of the East Coast of India

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

Cited by 29 publications

References 28 publications

High‐resolution X‐ray fluorescence profiling of hybrid event beds: Implications for sediment gravity flow behaviour and deposit structure

High‐resolution X‐ray fluorescence profiling of hybrid event beds: Implications for sediment gravity flow behaviour and deposit structure

An Empirical Method to Predict Sediment Grain Size From Inorganic Geochemical Measurements

The Indian Sundarbans: Biogeochemical Dynamics and Anthropogenic Impacts

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