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In the context of examining the potential usage of safe and sustainable geothermal energy in the Alberta Basin, whether in deep sediments or crystalline rock, the understanding of the in situ stress state is crucial. It is a key challenge to estimate the 3-D stress state at an arbitrarily chosen point in the crust, based on sparsely distributed in situ stress data.
To address this challenge, we present a large-scale 3-D geomechanical-numerical model (700 km x 1200 km x 80 km) from a large portion of the Alberta Basin, to provide a 3-D continuous quantification of the contemporary stress orientations and stress magnitudes. To calibrate the model, we use a large database of in situ stress orientation (321 S-Hmax) as well as stress magnitude data (981 S-V, 1720 S-hmin and 2 (+11) S-Hmax) from the Alberta Basin. To find the best-fit model, we vary the material properties and primarily the displacement boundary conditions of the model. This study focusses in detail on the statistical calibration procedure, because of the large amount of available data, the diversity of data types, and the importance of the order of data tests.
The best-fit model provides the total 3-D stress tensor for nearly the whole Alberta Basin, and allows estimation of stress orientation and stress magnitudes in advance of any well. First-order implications for the well design and configuration of enhanced geothermal systems are revealed. Systematic deviations of the modelled stress from the in situ data are found for stress orientations in the Peace River and the Bow Island Arch as well as for leak-off test magnitudes.
The present study proposes a General Probabilistic Framework (GPF) for uncertainty and global sensitivity analysis of deterministic models in which, in addition to scalar inputs, non-scalar and correlated inputs can be considered as well. The analysis is conducted with the variance-based approach of Sobol/Saltelli where first and total sensitivity indices are estimated. The results of the framework can be used in a loop for model improvement, parameter estimation or model simplification. The framework is applied to SWAP, a 113 hydrological model for the transport of water, solutes and heat in unsaturated and saturated soils. The sources of uncertainty are grouped in five main classes: model structure (soil discretization), input (weather data), time-varying (crop) parameters, scalar parameters (soil properties) and observations (measured soil moisture). For each source of uncertainty, different realizations are created based on direct monitoring activities. Uncertainty of evapotranspiration, soil moisture in the root zone and bottom fluxes below the root zone are considered in the analysis. The results show that the sources of uncertainty are different for each output considered and it is necessary to consider multiple output variables for a proper assessment of the model. Improvements on the performance of the model can be achieved reducing the uncertainty in the observations, in the soil parameters and in the weather data. Overall, the study shows the capability of the GPF to quantify the relative contribution of the different sources of uncertainty and to identify the priorities required to improve the performance of the model. The proposed framework can be extended to a wide variety of modelling applications, also when direct measurements of model output are not available.
Lacustrine sediments have been widely used to investigate past climatic and environmental changes on millennial to seasonal time scales. Sedimentary archives of lakes in mountainous regions may also record non-climatic events such as earthquakes. We argue herein that a set of 64 annual laminae couplets reconciles a stratigraphically inconsistent accelerator mass spectrometry (AMS) C-14 chronology in a similar to 4-m-long sediment core from Lake Mengda, in the north-eastern Tibetan Plateau. The laminations suggest the lake was formed by a large landslide, triggered by the 1927 Gulang earthquake (M = 8.0). The lake sediment sequence can be separated into three units based on lithologic, sedimentary, and isotopic characteristics. Starting from the bottom of the sequence, these are: (1) unweathered, coarse, sandy valley-floor deposits or landslide debris that pre-date the lake, (2) landslide-induced, fine-grained soil or reworked landslide debris with a high organic content, and (3) lacustrine sediments with low organic content and laminations. These annual laminations provide a high-resolution record of anthropogenic and environmental changes during the twentieth century, recording enhanced sediment input associated with two phases of construction activities. The high mean sedimentation rates of up to 4.8 mm year(-1) underscore the potential for reconstructing such distinct sediment pulses in remote, forested, and seemingly undisturbed mountain catchments.
We apply and evaluate a recent machine learning method for the automatic classification of seismic waveforms. The method relies on Dynamic Bayesian Networks (DBN) and supervised learning to improve the detection capabilities at 3C seismic stations. A time-frequency decomposition provides the basis for the required signal characteristics we need in order to derive the features defining typical "signal" and "noise" patterns. Each pattern class is modeled by a DBN, specifying the interrelationships of the derived features in the time-frequency plane. Subsequently, the models are trained using previously labeled segments of seismic data. The DBN models can now be compared against in order to determine the likelihood of new incoming seismic waveform segments to be either signal or noise. As the noise characteristics of seismic stations varies smoothly in time (seasonal variation as well as anthropogenic influence), we accommodate in our approach for a continuous adaptation of the DBN model that is associated with the noise class. Given the difficulty for obtaining a golden standard for real data (ground truth) the proof of concept and evaluation is shown by conducting experiments based on 3C seismic data from the International Monitoring Stations, BOSA and LPAZ.
AimFossil pollen spectra from lake sediments in central and western Mongolia have been used to interpret past climatic variations, but hitherto no suitable modern pollen-climate calibration set has been available to infer past climate changes quantitatively. We established such a modern pollen dataset and used it to develop a transfer function model that we applied to a fossil pollen record in order to investigate: (1) whether there was a significant moisture response to the Younger Dryas event in north-western Mongolia; and (2) whether the early Holocene was characterized by dry or wet climatic conditions.
LocationCentral and western Mongolia.
MethodsWe analysed pollen data from surface sediments from 90 lakes. A transfer function for mean annual precipitation (P-ann) was developed with weighted averaging partial least squares regression (WA-PLS) and applied to a fossil pollen record from Lake Bayan Nuur (49.98 degrees N, 93.95 degrees E, 932m a.s.l.). Statistical approaches were used to investigate the modern pollen-climate relationships and assess model performance and reconstruction output.
ResultsRedundancy analysis shows that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Spatial autocorrelation and significance tests of environmental variables show that the WA-PLS model for P-ann is the most valid function for our dataset, and possesses the lowest root mean squared error of prediction.
Main conclusionsPrecipitation is the most important predictor of pollen and vegetation distributions in our study area. Our quantitative climate reconstruction indicates a dry Younger Dryas, a relatively dry early Holocene, a wet mid-Holocene and a dry late Holocene.
A modern pollen dataset from China and Mongolia (18-52 degrees N, 74-132 degrees E) is investigated for its potential use in climate reconstructions. The dataset includes 2559 samples, 229 terrestrial pollen taxa and four climatic variables - mean annual precipitation (P-ann): 35-2091 mm, mean annual temperature (T-ann): -12.1-25.8 degrees C, mean temperature in the coldest month (Mt(co).): -33.8-21.7 degrees C, and mean temperature in the warmest month (Mt(wa)): 03-29.8 degrees C. Modern pollen-climate relationships are assessed using canonical correspondence analysis (CCA), Huisman-Olff-Fresco (HOF) models, the modern analogue technique (MAT), and weighted averaging partial least squares (WA-PLS). Results indicate that P-ann is the most important climatic determinant of pollen distribution and the most promising climate variable for reconstructions, as assessed by the coefficient of determination between observed and predicted environmental values (r(2)) and root mean square error of prediction (RMSEP). Mt(co) and Mt(wa) may be reconstructed too, but with caution. Samples from different depositional environments influence the performance of cross-validation differently, with samples from lake sediment-surfaces and moss polsters having the best fit with the lowest RMSEP. The better model performances of MAT are most probably caused by spatial autocorrelation. Accordingly, the WA-PLS models of this dataset are deemed most suitable for reconstructing past climate quantitatively because of their more reliable predictive power. (C) 2014 Elsevier B.V. All rights reserved.
Background and Aims Dynamic processes occurring at the soil-root interface crucially influence soil physical, chemical and biological properties at a local scale around the roots, and are technically challenging to capture in situ. This study presents a novel multi-imaging approach combining fluorescence and neutron radiography that is able to simultaneously monitor root growth, water content distribution, root respiration and root exudation.
Methods Germinated seeds of white lupins (Lupinus albus) were planted in boron-free glass rhizotrons. After 11 d, the rhizotrons were wetted from the bottom and time series of fluorescence and neutron images were taken during the subsequent day and night cycles for 13 d. The following day (i.e. 25 d after planting) the rhizotrons were again wetted from the bottom and the measurements were repeated. Fluorescence sensor foils were attached to the inner sides of the glass and measurements of oxygen and pH were made on the basis of fluorescence intensity. The experimental set-up allowed for simultaneous fluorescence imaging and neutron radiography.
Key Results The interrelated patterns of root growth and distribution in the soil, root respiration, exudation and water uptake could all be studied non-destructively and at high temporal and spatial resolution. The older parts of the root system with greater root-length density were associated with fast decreases of water content and rapid changes in oxygen concentration. pH values around the roots located in areas with low soil water content were significantly lower than the rest of the root system.
Conclusions The results suggest that the combined imaging set-up developed here, incorporating fluorescence intensity measurements, is able to map important biogeochemical parameters in the soil around living plants with a spatial resolution that is sufficiently high enough to relate the patterns observed to the root system.
The complementary advantages of GPS and seismic measurements are well recognized in seismotectonic monitoring studies. Therefore, integrated processing of the two data streams has been proposed recently in an attempt to obtain accurate and reliable information of surface displacements associated with earthquakes. A hitherto still critical issue in the integrated processing is real-time detection and precise estimation of the transient baseline error in the seismic records. Here, we report on a new approach by introducing the seismic acceleration corrected by baseline errors into the state equation system. The correction is performed and regularly updated in short epochs (with increments which may be as short as seconds), so that station position, velocity, and acceleration can be constrained very tightly and baseline error can be estimated as a random-walk process. With the adapted state equation system, our study highlights the use of a new approach developed for integrated processing of GPS and seismic data by means of sequential least-squares adjustment. The efficiency of our approach is demonstrated and validated using simulated, experimental, and real datasets. The latter were collected at collocated GPS and seismic stations around the 4 April 2010, E1 Mayor-Cucapah earthquake (Mw, 7.2). The results have shown that baseline errors of the strong-motion sensors are corrected precisely and high-precision seismic displacements are real-timely obtained by the new approach.
The age models of fluvio-lacustrine sedimentary sequences are often subject of discussions in paleoclimate research. The techniques employed to build an age model are very diverse, ranging from visual or intuitive estimation of the age-depth relationship over linear or spline interpolations between age control points to sophisticated Bayesian techniques also taking into account the most likely deposition times of the type of sediment within the sequence. All these methods, however, fail in detecting abrupt variations in sedimentation rates, including the possibility of episodes of no deposition (hiatus), which is the strength of the method presented in this work. The new technique simply compares the deposition time of equally thick sediment slices from the differences of subsequent radiometric age dates and the unit deposition times of the various sediment types. The percentage overlap of the distributions of these two sources of information, together with the evidence from the sedimentary record, helps to build an age model of complex sequences including abrupt variations in the rate of deposition including one or many hiatuses. (C) 2014 Elsevier B.V. All rights reserved.
Extracellular DNA (eDNA) is a ubiquitous biological compound in aquatic sediment and soil. Previous studies suggested that eDNA plays an important role in biogeochemical element cycling, horizontal gene transfer and stabilization of biofilm structures. Previous methods for eDNA extraction were either not suitable for oligotrophic sediments or only allowed quantification but no genetic analyses. Our procedure is based on cell detachment and eDNA liberation from sediment particles by sequential washing with an alkaline sodium phosphate buffer followed by a separation of cells and eDNA. The separated eDNA is then bound onto silica particles and purified, whereas the intracellular DNA from the separated cells is extracted using a commercial kit. The method provides extra- and intracellular DNA of high purity that is suitable for downstream applications like PCR. Extracellular DNA was extracted from organic-rich shallow sediment of the Baltic Sea, glacially influenced sediment of the Barents Sea and from the oligotrophic South Pacific Gyre. The eDNA concentration in these samples varied from 23 to 626 ng g(-1) wet weight sediment. A number of experiments were performed to verify each processing step. Although extraction efficiency is higher than other published methods, it is not fully quantitative. (C) 2014 Elsevier B.V. All rights reserved.