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.

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.

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.