The data assimilation process for geothermal reservoirs often relies on well data which primarily offers insights into the immediate vicinity of the borehole. However, integrating geophysical methods can provide valuable information beyond well proximity, possibly enhancing reservoir predictions. Electromagnetic methods can be sensitive to the decreasing conductivity from heat extraction in geothermal reservoirs. A scheme to incorporate electromagnetic data into a data assimilation process for geothermal reservoirs is presented and implemented in this study. First, an ensemble of prior models representing the reservoir uncertainty is used to determine the moments of the resulting temperature field using a forward geothermal simulation. Source and receiver locations are determined by maximizing the distance of the path through the expected temperature changes while ensuring that the source and receiver are not excessively distant. Subsequently, a conductivity model is implemented using an empirical relationship. The expected electric field response can then be simulated using an electromagnetic forward model. To assimilate the data, the Ensemble Smoother with the Multiple Data Assimilation (ES-MDA) method is employed. The findings demonstrate that the incorporation of electromagnetic data provides more information regarding the temperature field, which when combined with the localized data from the production well improves the temperature forecast accuracy of both the production well and the entire reservoir model.

The North German basin holds high potential for geothermal lithium exploration. Yet, so far, the assessment of reservoirs concentrates on locations in the Upper Rhine Graben. This thesis gives an example for the evaluation of a potential geothermal lithium reservoir of the Middle Buntsandstein at the location Horstberg in Lower Saxony. The targeted layers are part of the Solling and the Detfurth formation.

To determine the porosities and volumes of the two potential reservoir layers, including their uncertainties, well log data and a structural model are evaluated. The model is created using \textit{Gempy}, an open-source library to create 3-D models in Python. Based on the estimated parameters and data from the literature, the lithium and heat quantities stored in place are estimated. Furthermore, calculations are carried out that target the design and production volume of a geothermal doublet with a theoretical production rate of 24\,L/s.

For the Solling, the resulting mean quantities are 13,836\,t of lithium and a stored heat of 2.59 - 3.04\,GJ\,m$^{-2}$. The values of the Detfurth are much lower. The amount of stored lithium amounts to 3,773\,t and the heat-in-place is determined to be 1.85 - 2.19\,GJ\,m$^{-2}$. Regarding the calculations of the doublet, depending on the input parameters, the mean required distance between injection and production well is 1,037 - 2,021\,m. Based on these results, 8-28 doublets would fit in the study area. One doublet system could produce 540.6 - 660.5\,t of LCE per year.

Overall, the conditions for geothermal lithium exploration at Horstberg are rather poor. The reservoir is mainly limited by its low thickness and low production rate. Nevertheless, this study gave a good example of how to assess a potential geothermal lithium reservoir in the North German Basin.

During their service live reinforced and prestressed concrete structures experience deterioration. Therefore, to effectively assure the safety and durability of the compounds, non destructive testing methods, such as Ground Penetrating Radar, employed at high frequencies, provide a rapid assessment of the geometry of the compound. Although amplitude and traveltime evaluations are the most widely used techniques, attribute based analysis to enhance the interpretation quality has gained interest in recent years. A great quantity of surveys exist that attempt to determine the diameter of cylindrical targets such as reinforcement bars or tendon ducts. Indeed, the affiliation of a reflector to one of the two groups is still a challenging task. Therefore, different attribute approaches are applied to four data sets with high spatial sampling rates collected under controlled conditions, to derive features that unambiguously assign the considered reflection hyperbolas to either reinforcement bars or tendon ducts. For this purpose, B-scans are reduced to the individual reflectors by manual selecting the desired windows. Then, attributes are applied to the hyperbolas and the results are compared with each other to derive features describing the two reflector types. For attributes where features can be extracted, they are tested for measurements with relatively low spatial sampling rates and for one on-site data set. It is shown that the analysis provides sufficient classifications for six out of the nine tested attributes when data with high spatial sampling rates under controlled environment conditions are considered. In contrast, relatively low spatial sampling rates yield to accurate results for only three attributes: The maximum absolute amplitude, the cumulative energy and the amplitude at the apex. Although promising results for low spatial sampling rates are obtained, the application to on-site data is still complicated. Nevertheless, this study provides the basis for automated reflector classification.

The multi-pole PML (MPML) is tested on models that simulate seismic waves traveling through the subsurface. Using a recursive integration technique a stretching function consisting of the sum of multiple stretching functions is implemented in the velocity-stress finite difference time domain wave equations. The MPML is implemented in both the rotated staggered grid (RSG) and the Virieux grid. The performance of the MPML is tested on a square model, rectangular model and a rectangular model with a free-surface and compared to other types of PML’s implemented in these models. The main result is that the MPML can be implemented in the velocity stress wave equations giving stable results similar to other PML types.