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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.
The correct orientation of seismic sensors is critical for studies such as full moment tensor inversion, receiver function analysis, and shear-wave splitting. Therefore, the orientation of horizontal components needs to be checked and verified systematically. This study relies on two different waveform-based approaches, to assess the sensor orientations of the broadband network of the Kandilli Observatory and Earthquake Research Institute (KOERI). The network is an important backbone for seismological research in the Eastern Mediterranean Region and provides a comprehensive seismic data set for the North Anatolian fault. In recent years, this region became a worldwide field laboratory for continental transform faults. A systematic survey of the sensor orientations of the entire network, as presented here, facilitates related seismic studies. We apply two independent orientation tests, based on the polarization of P waves and Rayleigh waves to 123 broadband seismic stations, covering a period of 15 yr (2004-2018). For 114 stations, we obtain stable results with both methods. Approximately, 80% of the results agree with each other within 10 degrees. Both methods indicate that about 40% of the stations are misoriented by more than 10 degrees. Among these, 20 stations are misoriented by more than 20 degrees. We observe temporal changes of sensor orientation that coincide with maintenance work or instrument replacement. We provide time-dependent sensor misorientation correction values for the KOERI network in the supplemental material.
Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.
This study deals with the East Beni Suef Basin (Eastern Desert, Egypt) and aims to evaluate the source-generative potential, reconstruct the burial and thermal history, examine the most influential parameters on thermal maturity modeling, and improve on the models already published for the West Beni Suef to ultimately formulate a complete picture of the whole basin evolution.
Source rock evaluation was carried out based on TOC, Rock-Eval pyrolysis, and visual kerogen petrography analyses. Three kerogen types (II, II/III, and III) are distinguished in the East Beni Suef Basin, where the Abu Roash "F" Member acts as the main source rock with good to excellent source potential, oil-prone mainly type II kerogen, and immature to marginal maturity levels.
The burial history shows four depositional and erosional phases linked with the tectonic evolution of the basin. A hiatus (due to erosion or non-deposition) has occurred during the Late Eocene-Oligocene in the East Beni Suef Basin, while the West Beni Suef Basin has continued subsiding.
Sedimentation began later (Middle to Late Albian) with lower rates in the East Beni Suef Basin compared with the West Beni Suef Basin (Early Albian). The Abu Roash "F" source rock exists in the early oil window with a present-day transformation ratio of about 19% and 21% in the East and West Beni Suef Basin, respectively, while the Lower Kharita source rock, which is only recorded in the West Beni Suef Basin, has reached the late oil window with a present-day transformation ratio of about 70%.
The magnitude of erosion and heat flow have proportional and mutual effects on thermal maturity.
We present three possible scenarios of basin modeling in the East Beni Suef Basin concerning the erosion from the Apollonia and Dabaa formations.
Results of this work can serve as a basis for subsequent 2D and/or 3D basin modeling, which are highly recommended to further investigate the petroleum system evolution of the Beni Suef Basin.