Combining hyperspectral and XRF analyses to reconstruct high-resolution past flood frequency from lake sediments

<p>Due to global climate changes, an intensification of extreme events such as floods is expected in many regions, affecting an increasing number of people. An assessment of the flood frequencies is then a public concern. For several years now, numerous studies are undertaken on geological paleoclimate records and especially on lake sediments to understand the fluctuations of the flood activities in contrasting climatic contexts and over long time periods. Flood events produce turbidity currents in lake basins that will usually lead to a normal graded detrital layer that differs remarkably from the continuous sedimentation. Currently, in an overwhelming majority of studies, once identified, the layers with the same characteristics (e.g. texture, geochemical composition, grain-size) are usually counted by naked-eye observation. Unfortunately, this method is time-consuming, has a low spatial resolution potential and can lead to accuracy bias and misidentifications. To resolve these shortcomings, high-resolution analytical methods could be proposed, as X-ray computed tomography or hyperspectral imaging. When coupled with algorithms, hyperspectral imaging allows automatic identifications of these events.</p><p>Here, we propose a new method of flood layer identification and counting, based on the combination of two high-resolution techniques (hyperspectral imaging and high-resolution XRF core scanning). This approach was applied to one sediment core retrieved from the Lake Le Bourget (French Alps) in 2017. We use two hyperspectral sensors from the visible/near-infrared (VNIR, pixel size: 60 µm) and the short wave infrared (SWIR, pixel size: 200 µm) spectral ranges and several machine learning methods (decision tree and random forest, neural networks, and discriminant analysis) to extract instantaneous events sedimentary signal from continuous sedimentation. The study shows that the VNIR sensor is the optimal one to create robust classification models with an artificial neural network (prediction accuracy of 0.99). This first step allows the estimation of a classification map and then the reconstruction of a chronicle of the frequency and the thicknesses of the instantaneous event layers estimated.  </p><p>High-resolution XRF core scanning (XRF-CS) analyses were performed on the same core with a 200 µm step. Titanium (Ti) and Manganese (Mn) were selected as a high-resolution grain size indicator and a redox-sensitive element that shows abrupt inputs of oxygenated water-related to floods, respectively. Both elements have thus been added to the model in order to refine the chronicle derived from hyperspectral sensors. The combination of both hyperspectral and XRF-CS signal indicator allows to decipher floods from instantaneous deposits (e.g slump). This combined chronicle is in good agreement with the expected frequency obtained from the naked-eye chronicle realized on the same core (r² = 0.8). In this study, we present for the first time, an innovative approach based on machine learning which allows to propose fast automatized flood frequencies chronicles. This work was assessed by traditional deposits observations, but it can be easily applied to very micrometric deposits, undistinguishable to the naked eye. Finally, this model can be implemented with other indicators. It then represents a promising tool not only for flood reconstructions but also for other paleoenvironmental issues.</p>

Chemostratigraphy of Triassic successions on the southern Barents Sea Shelf – preliminary results from XRF Core Scanning.

<p>The Barents Sea Shelf on the north-western corner of the Eurasian plate has a complex geological history, comprising large-scale processes controlled by plate movements, climatic variations and changing depositional environments. During the last decades, as the search for hydrocarbons within the area gained increased interest, Triassic sequences have been the target of comprehensive investigations. In our project, we test the potential of improving the correlation of Triassic strata using X-ray fluorescence (XRF) core scanning of siliciclastic drill cores.</p><p>XRF core scanning is a frequently used method on soft sediment cores, e.g. within marine geology and palaeo-climate studies. However, the applicability of this method on drill cores from exploration wells from the hydrocarbon industry has not been tested so far. We use this method to establish geochemical stratigraphic parameters, as well as to contribute to the identification of provenances, reconstruct palaeo-envrionments, and support the correlation of drill cores. This provides a novel, fast, inexpensive, and non-destructive method to be applied in hydrocarbon exploration, as well as in studies of lithified siliciclastic sediments in general.</p><p>Triassic intervals from 24 shallow drill cores from the southern Barents Sea (Finnmark Platform, Nordkapp Basin, Svalis Dome, Maud Basin and Bjarmeland Platform) provide the basis for this study. The cores have previously been comprehensively studied and described by IKU (the Norwegian Continental Shelf Institute; today SINTEF Petroleum Research), and studies of provenance and palaeo-environment have also been performed (e.g. Vigran et al., 1986). This data makes it possible to compare the geochemical units established in this study with other stratigraphic information.</p><p>We present preliminary results of establishing geochemical units from XRF core scanning, and the use of these for correlation within known stratigraphic frameworks and between geographic areas, as well as to increase the understanding of changes in provenance and palaeo-environments within these successions in the Barents Sea.</p><p>References:<br>Vigran, J.O., G. Elvebakk, T.L. Leith, T. Bugge, V. Fjerdingstad, R.M. Goll, R. Konieczny, and A. Mørk. 1986. 'Dia-Structure Shallow Drilling 1986. Main data report. IKU Rep. No. 21.3420.00/04/86, 242 pp'.</p><p> </p>

Comparison and correction of element measurements using qualitative and quantitative X-ray fluorescence in lacustrine sediments: A case study of Lake Hurleg

Using an X-ray Fluorescence (XRF) core scanner with nondestructive and successive, the chemistry features of lacustrine sediment can be measured directly. This method of XRF core scanner measurements has been widely applied to core sediment analysis but uncertain of the precision and accuracy. Comparison of intensities obtained by XRF core scanning and the concentration measured by conventional X-ray Fluorescence, indicates effects of physical properties varied from different elements on elemental intensities in the lacustrine sediments of a core from Lake Hurleg in the northeastern Tibetan Plateau. Correlation among elements Ti and Sr between the two measurement methods of the XRF and the conventional XRF is high. Using the intensity of Cl as an indicator of water content, the element intensities of Ti and Sr in the core samples is corrected. But the correlation coefficients of Ti and Sr is litter raised. The results show that XRF core scanning is a very useful tool for measuring element concentration in sediments particularly for high intensities elements.