Department of Earth SciencesAcronym | D-ERDW | Homepage | https://erdw.ethz.ch/ | Country | Switzerland | ZIP, City | 8092 Zurich | Address | Sonneggstrasse 5 | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | Department of Earth Sciences | Child organizations | |
Open OpportunitiesTo interpret new observations of exoplanets using telescopes, a better understanding of how gases at high pressures and temperatures mix in their atmospheres is required. The goal of this project is to develop more accurate models for mixtures of major gases in planetary atmospheres at extreme conditions and apply them to interpret recent spectra collected for sub-Neptune planets. - Astronomy and Astrophysics, Chemical Thermodynamics and Energetics, Geochemistry
- Master Thesis
| Debris flows are extremely rapid, flow-like landslides composed of boulders, woody debris as well as a viscous
slurry. They are an important geomorphic process which transport sediment to the river system as well as a
significant hazard to infrastructure and people. For the process characterization, hazard assessment and early
warning, debris-flow monitoring is important but challenged by the harsh conditions of alpine environment in which
they occur. One monitoring technique is with cameras, for which the output is useful for a number of applications
such velocity estimations or object detection techniques. However, the fast movements and various light conditions
often lead to low-quality data.
In this project, a debris-flow monitoring setup will be deployed in the lab. The setup includes a high-speed camera
and a small flume (Fig. 1), where small debris flows can be artificially initiated. The student will conduct a series of
tests in different light conditions and camera settings. The recordings will be used to estimate debris-flow velocities
through Particle Image Velocimetry (PIV, e.g., Theule et al., 2018), which is an optical method often used to estimate
flow velocities at high resolution (Fig. 2). Finally, the student will identify the optimal camera settings as well as
associated uncertainties in different light conditions. These experiments will provide important insights to the
accuracy of the devices and methods used, as well as support future decisions regarding monitoring setups. | For engineering geological purposes, one often needs to characterize and classify a sediment or soil material, which requires the assessment of a set of parameters like grain size, grain shape, angularity. However, obtaining reliable and representative data for these parameters can be challenging, especially for large areas or complex terrains. Traditional methods of mapping and classification often require manual labor and field work, which can be time-consuming, costly, and – particularly in the absence of laboratory testing – be prone to human error or bias. Moreover, these methods may not capture the spatial variability and heterogeneity of the sediment or soil properties across the area of interest.
To be able to cover larger areas we use drones to obtain high resolution optical images and elevation data. These images can then be processed using a machine-learning assisted image segmentation technique, allowing to extract the outlines of individual sediment grains for the entire area (Fig. 1). Based on these outlines, we can then compute various parameters for each grain, such as its size, shape, angularity, and orientation. Furthermore, we can analyze the distribution and statistics of these parameters for the whole area and use them to identify and describe the sediment characteristics for different sub-regions.
In this project, we apply this drone-based approach to the landslide deposit that resulted from the event that occurred in Brienz/Brinzauls on June 15th 2023 (Fig. 2). The student will apply an existing python script and workflow to this data set and extract grain size distributions and potentially other parameters. Prior knowledge of python is not required and the student would not need to write much new own code, but an interest in coding is advantageous. Furthermore, a GIS software (QGIS or ArcGIS Pro) and potentially a photogrammetry software for drone footage analysis will be used. Depending on the exact timing of the project and the weather conditions at that moment a few days of field work will be planned to either acquire additional drone footage or ground truth the obtained sediment distributions.
| Geothermal energy is playing a pivotal role in the transition of the Swiss energy sector from reliance on fossil fuels and nuclear power to sustainable sources. Aligning with this, the Bedretto Underground Laboratory serves as a testbed for hydraulic stimulation experiments that are essential for enhancing the permeability of Enhanced Geothermal Systems (EGS). In addition to hydraulic stimulations, the temperature discrepancy between the injection fluid and the hot reservoir rock can induce thermal fractures due to rapid cooling near the injection wells and slower cooling in the more distant field. It is vital to quantify the deterioration of the reservoir rock mechanical strength caused by thermal cracking to ensure the structural integrity of injection and production wells in EGS.
This study aims to investigate the mechanical response of Rotondo Granite, sourced from the Bedretto lab. Uniaxial compressive strength (UCS) tests will be performed on specimens treated under various temperature and cooling conditions at the Rock Mechanics and Physics Lab (RPM Lab) (Figure 1b). Heat treatments will include increasing the temperature from 25 °C to 800 °C using a high temperature furnace, followed by both rapid and slow cooling. The results of this study will provide a comprehensive data set describing the mechanical strength deterioration of Rotondo Granite following heating and cooling treatments. The student will learn how to prepare rock specimens, conduct UCS tests, and interpret the mechanical properties (Young’s Modulus, Poisson’s Ratio, and UCS) of the reservoir rock according to ISRM standards. Necessary training will be provided on preparing the testing specimens, setting up testing equipment, data acquisition systems and analyzing the test results.
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