Sedimentary rocks can record signals produced by highly complex processes. These signals are generated by a progressive deposition of sediments which can be affected, mainly through the climate system, by regular astronomical cycles (i.e. Milankovitch cycles), and by irregular oscillations like the El Niño-Southern Oscillation. Also, usually through biological, chemical and/or physical post-depositional processes, the sedimentary records can be affected by pattern-creating heterogeneous processes. The noise in the signals further complicates the records, and the deposition rate (or sedimentation rate) can fluctuate, which greatly reduces the effectiveness of the classical stationary time-series analysis methods commonly used in cyclostratigraphy (i.e. the study of the cycles found in the sedimentary records). Faced with this multiplicity of processes, a common approach used in cyclostratigraphy is to reduce each signal to more manageable sub-signals, either over a given range of frequencies (e.g., by filtering), or by considering a continuum of constant frequencies (e.g., using transforms). This makes it possible to focus on the features of interest, commonly astronomical cycles. However, working with sub-signals is not trivial. Firstly, sub-signals have a certain amount of cross-cancellation when they are summed back to reconstruct the initial signal. This means that in filters and in transforms, wiggles that are not present in the initial signal can appear in the sub-signals. Secondly, the sub-signals considered often cannot be summed to reconstruct the initial signal: this means that there are processes affecting the signal which remain unstudied. It is possible to take cross-cancellation into account and to consider the entire content of a signal by dividing the signal into a decomposition: a set of sub-signals that can be added back together to reconstruct the original signal. We discuss here how to reframe commonly used time-series analysis techniques in the context of decomposition, how they are affected by cross-cancellation, and how adequate they are for comprehending the whole signals. We also show that decomposition can be carried out by non-stationary time-series methods, which can minimise cross-cancellation, and have now reached sufficient maturity to tackle sedimentary records signals. We present novel tools to adapt non-stationary decomposition for cyclostratigraphic purposes, based on the concepts of Empirical Mode Decomposition (EMD) and Instantaneous Frequency (IF), mainly: (1) a fast Ensemble Empirical Mode Decomposition (EEMD) algorithm, (2) quality metrics for decomposition, and (3) plots to visualise instantaneous frequency, amplitude and frequency ratio. We illustrate the use of these tools by applying them on a greyscale signal from the site 926 of the Ocean Drilling Program, at Ceara Rise (western equatorial Atlantic), especially to identify and characterise the expression of astronomical cycles. The main goal is to show that by minimising cross-cancellation, we can apply in real signals what we call the wiggle-in-signal approach: making the sub-signals in the decomposition more representative of the expression, wiggle by wiggle, of all the processes affecting the signal (e.g., astronomical cycles). We finally argue that decomposition could be used as a practical standard output for time-series analysis interpretation of cyclostratigraphic signals.
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RBINS Staff Publications 2021
We present a workflow to conduct a full characterization of medium to coarse-grained igneous rocks, using portable, non-invasive, and reproducible approaches. This includes: (i) Image Analysis (IA) to quantify mineral phase proportions, grain size distribution using the Weka trainable machine learning algorithm. (ii) Portable X-ray fluorescence spectrometer (PXRF, Bruker Tracer IV) to quantify the whole-rock's chemical composition. For this purpose, a specific calibration method dedicated to igneous rocks using the open-source CloudCal app was developed. It was then validated for several key elements (Si, Al, K, Ti, Ca, Fe, Mn, Sr, Ga, Ba, Rb, Zn, Nb, Zr, and Y) by analyzing certified standard reference igneous rocks. (iii) Portable Magnetic Susceptibilimeter (pMS, Bartington MS2K system) to constrain the mineralogical contribution of the samples. The operational conditions for these three methods were tested and optimized by analyzing five unprepared surfaces of igneous rocks ranging from a coarse-grained alkaline granite to a fine-grained porphyric diorite and hence, covering variable grain sizes, mineralogical contents, and whole-rock geochemical compositions. For pMS and PXRF tools, one hundred analyses were conducted as a 10 cm × 10 cm square grid on each sample. Bootstrap analysis was implemented to establish the best grid size sampling to reach an optimized reproducibility of the whole-rock signature. For PXRF analysis, averaged compositions were compared to PXRF analysis on press-pellets and laboratory WD-XRF analysis on fused disk and solution ICP-OES (for major) and solution-ICPMS (for trace element concentrations). Ultimately, this workflow was applied in the field on granitoids from three Roman quarrying sites in the Lavezzi archipelago (southern Corsica) and tested against the Bonifacio granitic War Memorial, for which its provenance is established. Our results confirm this information and open the door to geoarchaeological provenance studies with a high spatial resolution.
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The empirical power law relation (PR) between resonance frequency (f0), obtained from H/V spectral ratio analysis of ambient noise, and sediment thickness (h), obtained from boreholes, is frequently used in microzonation studies to predict bedrock depth. In this study, we demonstrate (i) how to optimally construct a PR by including the error on the picked f0 in the regression, and (ii) how to evaluate a regression quality by identifying the under- or overestimation of the sediment thickness prediction. We apply this methodology on f0 data derived from 74 ambient noise recordings acquired above boreholes that reach the Brabant Massif bedrock below Brussels (Belgium). Separating the f0 data into different subset based on the cover geology does not significantly improve the bedrock depth prediction because the cover geology in Brussels has common base layers. In Brussels, the PR relation h = 88.631.f0−1.683 is the best candidate to convert f0 to depth, with a prediction error of 10%. The Brussels PR was subsequently applied on a local survey (404 measurements; 25 km2) in southern Brussels with the aim to study Brussels’ Brabant Massif bedrock paleorelief. By linking the obtained paleorelief, Bouguer gravity data and aeromagnetic data, a NNW-SSE oriented, 20 m-high subsurface ridge could be identified. This ridge stands out because of differential erosion between less-resistant and hard quartzitic rock formations of the Brabant Massif. This subsurface ridge deflects the local radiation of seismic energy resulting in an anomaly in the otherwise regional consistent azimuthal dependency of the resonance frequency. We conclude that adding a polarisation analysis to a microzonation survey analysis allows detecting anomalous features in the paleorelief.
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