In Geodesy, the development of accurate Digital Elevation Models of the surface helps to provide height data for various applications in other Earth Science disciplines. These models are used as base models for studies. Geophysics can be a support science when providing base models for non-invasive studies inside the Earth. Such models can be multi-dimensional velocity models, where the relation of velocities and depth can be used for other structural parameter studies in e.g. Geodynamics or Geochemistry. With technological advancements in the recent decade, there is incentive to provide new regional continental-scale models. Full-waveform inversion (FWI) is an imaging method that iteratively inverts through seismic waveforms for structural heterogeneities in the Earth to create a high resolution static im- age. The development of event-specific adaptive SmoothieSEM meshes for regional meshes leads to a substantial decrease in computational costs of the FWI by lowering the amount of mesh elements needed to model a wavefield. This allows to push regional continental-scale tomographies towards period bands that were previously cost-prohibitive. Divided into two tomographic studies, on a bigger continental-scale mesh of Europe and Western Asia and a smaller mesh focused on Europe respectively, the study areas are lowered to a period of 24s. The reference base model is derived from the current generation of the Collaborative Seismic Earth Model (CSEM). Starting at a minimum period of 50s, the mimimum period is pushed in steps towards the target of 24s in a multiscale inversion approach to enable monotonic misfit reduction. The misfit optimization of the synthetic and observed waveforms is based on time-frequency phase misfits while the gradient optimization is based on the trust-region limited memory Broyden-Fletcher-Goldfarb-Shannon (L-BFGS) method and dynamic mini- batches. Waveform fits are quantified by the reduction of the time-frequency phase misfit and normalized amplitude difference misfits between the end model and the CSEM base model. The waveform inversion reveals more heteregeneous structures in Europe and Western Asia compared to previous studies.

Landslides pose a risks to the communities settled in their vicinity. They pose a threat to human lives and significant economical damage to the affected communities. As the scientific community agrees that the climate change will further increase the risks associated with landslides, it is important to develop a reliable, cost effective and widely applicable technique to assess the stability of the potentially unstable slopes. Such a technique could allow to create an early warning systems meant to help evacuate the communities at risk before the landslide event happens. There have been many studies applying different methods of achieving this goal. This study will apply electrical resistivity tomography to investigate the subsurface and use machine learning methods in the form of supervised and unsupervised learning to interpret the measurements. The electrical resistivity tomography is a widely applied technique to investigate the stability of slopes. It can determine the water table in the subsurface, which plays the crucial role in the stability of the slopes. However, the manual interpretation of the electrical resistivity tomography profiles is a time-consuming and cumbersome task. In order to develop a reliable early warning system both the measurements and their interpretation have to be automatized. The first one has been already achieved. There are systems available that can perform electrical resistivity tomography automatically and continuously. In order to automatize the second part, the following research investigated the capability of machine learning to interpret resistivity profiles alongside with various methods of improving the accuracy of the results. The results show that machine learning is capable of performing this task. Some limitations were identified and guidelines for the preprocessing of the data were proposed. Several areas that require further investigation were identified.

Measuring temperature variations of the deep ocean is necessary to evaluate the heat flux between the atmosphere and the hydrosphere and thus calibrate climate change models. In the last two decades a passive alternative to profiling oceanographic floats has emerged: hydroacoustic thermometry. This method consists of using the oceanic ambient noise field as a source of acoustic waves and hydrophone arrays as receivers. These sensors are part of the International Monitoring System which is in place for the verification of the Comprehensive Nuclear-Test-Ban Treaty. They are positioned at a water depth of approximately 1 km, in the Sound Fixing and Ranging channel. This channel is a low-velocity layer that functions as an acoustic wave guide, thus facilitating very long range propagation with little attenuation. This study analyses transient signals between 2005 and 2018 triggered by submarine earthquakes and detected at station H10, situated near Ascension Island in the South Atlantic Ocean. This station consists of two three-element (triplet) arrays with an aperture of approximately 2 km each. The triplets are 126 km apart. Array processing techniques applied to individual triplets are prone to be biased by local conditions of the array surroundings. We demonstrate that this bias is largely suppressed when jointly processing both triplets as one six-elements array. Due to the malfunction of element S1, data quality decreased after October 2013 and our results are less robust. For the 2005-2013 period, we retrieve a temperature of 4.3 to 4.8 °C.

The active layer thickness has become an important indicator in climate change research as permafrost degradation has long been documented. The thawing of permafrost causes the release of greenhouse gases accelerating Arctic warming. Monitoring and quantifying spatial and temporal changes of the active layer are challenging but crucial for reliable climate projections. Geophysical methods offer a non-invasive investigation of electrical properties and their distribution in permafrost areas, revealing phase transitions from water to ice. Subsurface electrical resistivity images can be obtained through inversion of electromagnetic data, yet are inherently ambiguous because of the ill-posed nature of the inverse problem. Since regularization methods offer the possibility to stabilize the inversion, lateral and spatial constraints are incorporated in the inversion algorithm to produce quasi-2D and quasi-3D subsurface models. The developed methodology is evaluated based on synthetic data sets to determine suitable inversion parameters, which are subsequently applied to a field example from the Seward Peninsula, Alaska. Laterally constrained inversion methods based on a few-layer starting model succeed in resolving sharp interfaces in quasi-layered environments. In more complex settings minimum-structure models can retrieve accurate subsurface representations leveraging on vertical and horizontal smoothness constraints. Enforcing lateral and spatial consistency between neighboring soundings thereby yields a similar degree of model smoothness. The inverted field data confirms the conclusions drawn from the synthetic study, as meaningful three-layered models with regard to electrical resistivities are recovered, indicating resistive snow overlying the conductive active layer and highly resistive permafrost. However, the inversion results imply that the snow layer has a significant effect on the predicted model. The implemented constraints help in reducing the ambiguity of the models, but uncertainties introduced by limited data availability cannot be overcome. The potential of adopting spatial and lateral constraints to the inversion is shown, although it becomes evident that additional a priori information needs to be integrated in the objective function in order to comprehensively image the active layer.

Geophysical methods are widely used to gather information about the subsurface as they are nonintrusive and comparably cheap during acquisition, however, the solution to the geophysical inverse problem is inherently non-unique which introduces considerable uncertainties. Therefore, independently acquired geophysical data sets can be jointly inverted to reduce ambiguities in the resulting multi-physical subsurface images. Zhdanov et al. (2022) introduce a novel cooperative inversion approach using joint minimum entropy constraints in the regularization term of the objective functionals to create more consistent multi-physical images with sharper boundaries. Here, this approach is implemented in an open-source software and its applicability on electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and magnetic data is investigated. A synthetic 2D ERT and SRT data study is used to demonstrate the approach and to investigate the influence of the governing parameters. The findings showcase the advantage of the joint minimum entropy (JME) stabilizer over separate, conventional smoothness-constrained inversions. The method is then used to analyze field data from Rockeskyller Kopf, Germany. 3D ERT and magnetic data is combined and results confirm the expected volcanic diatreme structure with improved details. The multi-physical images of both methods are consistent in some regions as similar boundaries are produced in the resulting models, which have been lacking in previous studies. Because of its sensitivity to hydrologic conditions in the subsurface, observations suggest that the ERT method senses different structures than the magnetic method. However, these structures in the ERT result do not seem to be enforced on the magnetic susceptibility distribution, showcasing the flexibility of the approach. Both investigations outline the importance of a suitable parameter and reference model selection for the performance of the approach and suggest careful parameter tests prior to the joint inversion. With proper settings, the JME inversion is a promising tool for geophysical imaging, however, this thesis also lists some objectives for future studies and additional research to explore and optimize the method.

Landmines and Improvised Explosive Devices (IEDs) are among the most dangerous weapons found in (former) conflict areas. Many of them have only minimal or even no metal built in making it difficult to detect such devices via conventional metal detectors. Thus, alternative methods such as Ground Penetrating Radar (GPR) are deployed for this purpose. The field methodology of using GPR for the detection of buried objects generally includes varying degrees of inclination and height of the antenna in use. In this work, the influence of antenna inclination and height above ground is studied by using control studies with simple reflector geometries. The investigations shall provide more insights on the influence of these factors via measurements in controlled environments and numerical simulations. A comparison of the two is used to study the accuracy of numerical simulations of scenarios involving the detection of buried objects with GPR. The motivation hereby is the fact that gaining experimental data is generally very costly, time consuming and difficult to set up. Evaluations of the tested parameters showed the individual impact of each factor on the measured data. Height and inclination as well as exogenous factors, such as antenna orientation, subsurface structure and reflector geometry played a governing role influencing the measured data. The field measurements were successfully reproduced in numerical simulations. However, it is crucial to implement proper physical and geometrical parameters of the antenna, subsurface and reflector into the simulation environment to achieve realistic and reliable results. The findings of this thesis will be beneficial to implement more accurate numerical simulations in the future, which enables to study more complex, extensive and variable scenarios in a much faster, efficient and less costly manner.

Geophysical monitoring is a popular tool in aquifer characterization and groundwater flow. To address this objective at groundwater extraction site ‘t Klooster, an ERT dataset was analyzed to identify groundwater flow patterns resulting from the injection of warm oxygenated water. Using a petrophysical model, changes in resistivity were converted to estimated temperature changes to visualize the spread of warm oxygenated water. Multi-dimensional analysis of the resistivity response of the subsurface was carried out. This allowed for the division of the subsurface into 4 depth regimes according to their response to well activity. It is shown that wells up to 100m removed from the ERT set-up influenced the temperature distribution. Furthermore, injected oxygenated water highlighted a preferential flow path between the depths of 20 and 35m in a north-west direction. This is in line with global groundwater flow in the area. Groundwater flow effects could not be reliably separated from the effect of well activity, however its effect is recognized both during extraction of groundwater and injection of warm water.

The Bedretto Laboratory, located within the Rotondo granite, conducts stimulations of the host rock to investigate the possibility of enhanced geothermal energy and tries to better understand the risk of induced seismicity. Seismic anisotropy plays an important role when processing seismic data, making its characterization essential in such a laboratory. In a previous study, through crosshole seismic, the apparent velocity of P-waves was obtained and used to give some first values of the parameters characterising anisotropy assuming a Tilted Transversely Isotropic medium (TTI). Further investigating the obtained data, S-wave splitting could be detected. This phenomenon occurs in anisotropic media, resulting in horizontal S-waves (SH) and vertical S-waves (SV) having different seismic velocities. A sensitivity analysis was performed to assess the significance of S-wave information. The analysis revealed the benefit of incorporating the information provided by the S-waves to improve the current anisotropic model. Several crosshole surveys were carried out using different instruments, such as a S-wave sparker or a 3-component geophone. The aim was to understand the behavior of S-waves better and accurately identify the arrival time of both types of waves. The results reveal that rotating the 3-component geophones data was the best choice to isolate both types of S-waves. Time arrivals were picked, and apparent velocities were calculated knowing the ray path length. They show a change from 2747 m/s to 3216m/s for SH waves, and from 2830m/s to 3105m/s for SV-waves. An attempt was made to obtain the anisotropy parameters that successfully describe the observed velocities in the Rotondo granite.