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.

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 study of Gravitational Waves (GWs) opened a new window of possibilities to improve our understanding of the Universe. GWs provide suitable astronomical messengers for studying events that were not possible before through electromagnetic radiation, or in other cases complementing their observations. Ground-based interferometers like LIGO have been recording multiple GW events since the first detections in 2015. Despite the success of Earth-based observatories, the space limitations and noise sources on Earth point toward the need of building a spaceborne interferometer. The Laser Interferometer Space Antenna (LISA) is a planned project that will provide us with such a detector and will allow gaining access to lower frequency bands and more types of GW sources. To make the most out of LISA’s strengths, it is important to identify and develop alternative data analysis tools which are more appropriate for low latency searches of GWs than the current ones in use. Machine Learning techniques are a promising candidate since they can provide high accuracies, higher speeds, and a lower computational cost. Therefore, they can be used for the development of Low Latency Detectors (LLD) of GWs, which will be used to analyze the LISA recordings. I propose to build a prototype LLD by using a Sliding Window Algorithm, which makes use of Convolutional Neural Networks (CNNs) as its classification mechanism. To implement the LLD, I first create datasets composed of synthetic GW recordings of two different GW source types: Galactic Binaries (GBs) and Merging Blackhole Binaries (MBHBs). Then, I transform these recordings originally represented only in the time domain, into the frequency domain, and the time-frequency domain and train two different ML architectures (CNNs and Fully-Connected Neural Networks) using both the original and the transformed data. A performance evaluation is done to select the best combination of ML architecture and domain representation for solving the detection task. The chosen combination is then used as the classifier mechanism of the LLD acting in windows of five days duration. The LLD is tested on one-year-long recordings with different levels of noise. The analysis suggests that the time-frequency domain representations offer the most promising results for detecting both types of sources (GBs and MBHBs) reaching high accuracies in recordings with low to moderate signal-to-noise ratio (SNR).

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.