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Long- and short-term monitoring of a dam in response to seasonal changes and ground motion loading
(2021)
An experimental multi-parameter structural monitoring system has been installed on the Kurpsai dam, western Kyrgyz Republic. This system consists of equipment for seismic and strain measurements for making longer- (days, weeks, months) and shorter- (minutes, hours) term observations, dealing with, for example seasonal (longer) effects or the response of the dam to ground motion from noise or seismic events. Fibre-optic strain sensors allow the seasonal and daily opening and closing of the spaces between the dam's segments to be tracked. For the seismic data, both amplitude (in terms of using differences in amplitudes in the Fourier spectra for mapping the modes of vibration of the dam) and their time-frequency distribution for a set of small to moderate seismic events are investigated and the corresponding phase variabilities (in terms of lagged coherency) are evaluated. Even for moderate levels of seismic-induced ground motion, some influence on the structural response can be detected, which then sees the dam quickly return to its original state. A seasonal component was identified in the strain measurements, while levels of noise arising from the operation of the dam's generators and associated water flow have been provisionally identified.
Centroid moment tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally underdetermined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in nonlinear full CMT estimation for several simulated data sets and regional field data of the M-1 4.4, 2015 June 13 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.
Ice-rich permafrost has been subject to abrupt thaw and thermokarst formation in the past and is vulnerable to current global warming. The ice-rich permafrost domain includes Yedoma sediments that have never thawed since deposition during the late Pleistocene and Alas sediments that were formed by previous thermokarst processes during the Lateglacial and Holocene warming. Permafrost thaw unlocks organic carbon (OC) and minerals from these deposits and exposes OC to mineralization. A portion of the OC can be associated with iron (Fe), a redox-sensitive element acting as a trap for OC. Post-depositional thaw processes may have induced changes in redox conditions in these deposits and thereby affected Fe distribution and interactions between OC and Fe, with knock-on effects on the role that Fe plays in mediating present day OC mineralization. To test this hypothesis, we measured Fe concentrations and proportion of Fe oxides and Fe complexed with OC in unthawed Yedoma and previously thawed Alas deposits. Total Fe concentrations were determined on 1,292 sediment samples from the Yedoma domain using portable X-ray fluorescence; these concentrations were corrected for trueness using a calibration based on a subset of 144 samples measured by inductively coupled plasma optical emission spectrometry after alkaline fusion (R (2) = 0.95). The total Fe concentration is stable with depth in Yedoma deposits, but we observe a depletion or accumulation of total Fe in Alas deposits, which experienced previous thaw and/or flooding events. Selective Fe extractions targeting reactive forms of Fe on unthawed and previously thawed deposits highlight that about 25% of the total Fe is present as reactive species, either as crystalline or amorphous oxides, or complexed with OC, with no significant difference in proportions of reactive Fe between Yedoma and Alas deposits. These results suggest that redox driven processes during past thermokarst formation impact the present-day distribution of total Fe, and thereby the total amount of reactive Fe in Alas versus Yedoma deposits. This study highlights that ongoing thermokarst lake formation and drainage dynamics in the Arctic influences reactive Fe distribution and thereby interactions between Fe and OC, OC mineralization rates, and greenhouse gas emissions.
How biased are our models?
(2021)
Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.
This study is trying to understand the pre-eruptive magma storage and crystallization conditions of the Middle Miocene aged, silica-saturated trachytic rocks of the Afyon Volcanic Complex (AVC) in Western Anatolia, Turkey. Those rocks can be divided by their high K2O, K2O/Na2O ratio and Mg# into two groups, namely the intermediate-potassic (IPG) and the ultrapotassic (UPG). Here we are comparing calculated pressure (P) - temperature (T) conditions derived from geothermobarometric calculations of natural samples with results of high-pressure, high-temperature phase equilibria experiments. IPG samples are richer in silica (57-64 wt% SiO2), whereas UPG samples show intermediate SiO2 contents of 56-58 wt%. UPG are having high K2O contents ((>)9 wt %), K2O/Na2O ratios ((>)10 wt%) and Mg# values (75-77). IPG phenocrysts comprise plagioclase + biotite + amphibole + clinopyroxene +/- orthopyroxene +/- sanidine +/- phlogopite and oxides, while UPG mineralogical assemblage consists of amphibole + phlogopite + clinopyroxene + olivine + sanidine and oxides. IPG and UPG are enriched in Large-Ion Lithophile Elements (LILE), and both have negative anomalies in Nb, Sr, Zr and Ti elements. Additionally, IPG shows positive anomalies in Pb. Both IPG and UPG display enrichment in Light Rare Earth Elements (LREE), while IPG shows a more significant negative anomaly in Eu when compared to UPG. Plagioclase fractionation may play a role in magma generation. In IPG samples Ni and Cr values range between (3.3-18.8 ppm) and (2.6-27.8 ppm), respectively; whereas UPG samples have (119.1-120.7 ppm) Ni and (212.1-219.9 ppm) Cr. Dy/Yb ratios of IPG and UPG are higher than 2 and may indicate that garnet was present in the source. Geothermobarometric calculations for natural IPG clinopyroxene-melt pairs imply higher PT conditions (Dogan-Kulahci et al., 2015), while in this study high-pressure/high-temperature (HP/HT) phase equilibria experiments recreated the natural mineral assemblage at 2-4 kbar, 6-9 km and c. 900 degrees C. New plagioclase-melt calculations have confirmed lower mean magma storage temperatures, which are closer to the experimental results but still slightly elevated. Thus, trace element results of the natural rocks and experimental data may imply that a deep garnet-bearing magma source mixed with shallower magmas (IPG) was feeding the volcanic eruption.
Cyanobacteria are important primary producers in temperate freshwater ecosystems. However, studies on the seasonal and spatial distribution of cyanobacteria in deep lakes based on high-throughput DNA sequencing are still rare. In this study, we combined monthly water sampling and monitoring in 2019, amplicon sequence variants analysis (ASVs; a proxy for different species) and quantitative PCR targeting overall cyanobacteria abundance to describe the seasonal and spatial dynamics of cyanobacteria in the deep hard-water oligo-mesotrophic Lake Tiefer See, NE Germany. We observed significant seasonal variation in the cyanobacterial community composition (p < 0.05) in the epi- and metalimnion layers, but not in the hypolimnion. In winter-when the water column is mixed-picocyanobacteria (Synechococcus and Cyanobium) were dominant. With the onset of stratification in late spring, we observed potential niche specialization and coexistence among the cyanobacteria taxa driven mainly by light and nutrient dynamics. Specifically, ASVs assigned to picocyanobacteria and the genus Planktothrix were the main contributors to the formation of deep chlorophyll maxima along a light gradient. While Synechococcus and different Cyanobium ASVs were abundant in the epilimnion up to the base of the euphotic zone from spring to fall, Planktothrix mainly occurred in the metalimnetic layer below the euphotic zone where also overall cyanobacteria abundance was highest in summer. Our data revealed two potentially psychrotolerant (cold-adapted) Cyanobium species that appear to cope well under conditions of lower hypolimnetic water temperature and light as well as increasing sediment-released phosphate in the deeper waters in summer. The potential cold-adapted Cyanobium species were also dominant throughout the water column in fall and winter. Furthermore, Snowella and Microcystis-related ASVs were abundant in the water column during the onset of fall turnover. Altogether, these findings suggest previously unascertained and considerable spatiotemporal changes in the community of cyanobacteria on the species level especially within the genus Cyanobium in deep hard-water temperate lakes.
The Kohat fold and thrust belt in Pakistan shows a significantly different structural style due to the structural evolution on the double décollement compared to the rest of the Subhimalaya. In order to better understand the spatio-temporal structural evolution of the Kohat fold and thrust belt, we combine balanced cross sections with apatite (U?Th-Sm)/He (AHe) and apatite fission track (AFT) dating. The AHe and AFT ages appear to be totally reset, allowing us to date exhumation above structural ramps. The results suggest that deformation began on the frontal Surghar thrust at-15 Ma, predating or coeval with the development of the Main Boundary thrust at-12 Ma. Deformation propagated southward from the Main Boundary thrust on double de?collements between 10 Ma and 2 Ma, resulting in a disharmonic structural style inside the Kohat fold and thrust belt. Thermal modeling of the thermochronologic data suggest that samples inside Kohat fold and thrust belt experienced cooling due to formation of the duplexes; this deformation facilitated tectonic thickening of the wedge and erosion of the Miocene to Pliocene foreland strata. The spatial distribution of AHe and AFT ages in combination with the structural forward model suggest that, in the Kohat fold and thrust belt, the wedge deformed in-sequence as a supercritical wedge (-15-12 Ma), then readjusted by out-sequence deformation (-12-0 Ma) within the Kohat fold and thrust belt into a sub-critical wedge.
Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe
(2020)
Subsurface contamination due to excessive nutrient surpluses is a persistent and widespread problem in agricultural areas across Europe. The vulnerability of a particular location to pollution from reactive solutes, such as nitrate, is determined by the interplay between hydrologic transport and biogeochemical transformations. Current studies on the controls of subsurface vulnerability do not consider the transient behaviour of transport dynamics in the root zone. Here, using state-of-the-art hydrologic simulations driven by observed hydroclimatic forcing, we demonstrate the strong spatiotemporal heterogeneity of hydrologic transport dynamics and reveal that these dynamics are primarily controlled by the hydroclimatic gradient of the aridity index across Europe. Contrasting the space-time dynamics of transport times with reactive timescales of denitrification in soil indicate that similar to 75% of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least onethird of the year. We find that neglecting the transient nature of transport and reaction timescale results in a great underestimation of the extent of vulnerable regions by almost 50%. Therefore, future vulnerability and risk assessment studies must account for the transient behaviour of transport and biogeochemical transformation processes.
Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe
(2020)
Subsurface contamination due to excessive nutrient surpluses is a persistent and widespread problem in agricultural areas across Europe. The vulnerability of a particular location to pollution from reactive solutes, such as nitrate, is determined by the interplay between hydrologic transport and biogeochemical transformations. Current studies on the controls of subsurface vulnerability do not consider the transient behaviour of transport dynamics in the root zone. Here, using state-of-the-art hydrologic simulations driven by observed hydroclimatic forcing, we demonstrate the strong spatiotemporal heterogeneity of hydrologic transport dynamics and reveal that these dynamics are primarily controlled by the hydroclimatic gradient of the aridity index across Europe. Contrasting the space-time dynamics of transport times with reactive timescales of denitrification in soil indicate that similar to 75% of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least onethird of the year. We find that neglecting the transient nature of transport and reaction timescale results in a great underestimation of the extent of vulnerable regions by almost 50%. Therefore, future vulnerability and risk assessment studies must account for the transient behaviour of transport and biogeochemical transformation processes.
Rapidly growing seismic and macroseismic databases and simplified access to advanced machine learning methods have in recent years opened up vast opportunities to address challenges in engineering and strong motion seismology from novel, datacentric perspectives. In this thesis, I explore the opportunities of such perspectives for the tasks of ground motion modeling and rapid earthquake impact assessment, tasks with major implications for long-term earthquake disaster mitigation.
In my first study, I utilize the rich strong motion database from the Kanto basin, Japan, and apply the U-Net artificial neural network architecture to develop a deep learning based ground motion model. The operational prototype provides statistical estimates of expected ground shaking, given descriptions of a specific earthquake source, wave propagation paths, and geophysical site conditions. The U-Net interprets ground motion data in its spatial context, potentially taking into account, for example, the geological properties in the vicinity of observation sites. Predictions of ground motion intensity are thereby calibrated to individual observation sites and earthquake locations.
The second study addresses the explicit incorporation of rupture forward directivity into ground motion modeling. Incorporation of this phenomenon, causing strong, pulse like ground shaking in the vicinity of earthquake sources, is usually associated with an intolerable increase in computational demand during probabilistic seismic hazard analysis (PSHA) calculations. I suggest an approach in which I utilize an artificial neural network to efficiently approximate the average, directivity-related adjustment to ground motion predictions for earthquake ruptures from the 2022 New Zealand National Seismic Hazard Model. The practical implementation in an actual PSHA calculation demonstrates the efficiency and operational readiness of my model. In a follow-up study, I present a proof of concept for an alternative strategy in which I target the generalizing applicability to ruptures other than those from the New Zealand National Seismic Hazard Model.
In the third study, I address the usability of pseudo-intensity reports obtained from macroseismic observations by non-expert citizens for rapid impact assessment. I demonstrate that the statistical properties of pseudo-intensity collections describing the intensity of shaking are correlated with the societal impact of earthquakes. In a second step, I develop a probabilistic model that, within minutes of an event, quantifies the probability of an earthquake to cause considerable societal impact. Under certain conditions, such a quick and preliminary method might be useful to support decision makers in their efforts to organize auxiliary measures for earthquake disaster response while results from more elaborate impact assessment frameworks are not yet available.
The application of machine learning methods to datasets that only partially reveal characteristics of Big Data, qualify the majority of results obtained in this thesis as explorative insights rather than ready-to-use solutions to real world problems. The practical usefulness of this work will be better assessed in the future by applying the approaches developed to growing and increasingly complex data sets.