To run Nature’s Heat geothermal operations (Kwintsheul, The Netherlands) in a safe and efficient manner, and with the support of the local population, it is important to mitigate potential hazards. A well known potential hazard related to geothermal operations is the occurrence of induced seismicity due to injection and extraction of fluids in and from the subsurface. Monitoring the subsurface allows detecting the induced seismicity due to the geothermal operations. To monitor the seismic activity around the geothermal field of Nature’s Heat project, a passive seismic network was installed by Delft University of Technology, Seismoctech (Greece), and Gastreat- ment Services BV. This network has detected and located one single weak event on July 14, 2019.

Within this bachelor thesis, a beamforming technique is adopted to provide a framework that can be used to detect seismic events recorded by the passive seismic network. The goal is to separate coherent signals from noise and characterize the signal (e.g., estimate propagation direction). The objective of this bachelor thesis is to assess whether beamforming is capable of lowering the detection threshold, and determining the signal’s apparent velocity and back azimuth, using data recorded by the Kwintsheul seismic array. For applying the beam- forming technique, Kwintsheul data was implemented in a python code, originated from Remote Online Sessions for Emerging Seismologists (ROSES).

Following applying the beamforming technique, the results of the detection, and the parameter estimation of the back azimuth and apparent velocity, are given for the P-wave and S-wave, 300.96 deg. 6.43km/s and 315 deg. 3.31km/s, respectively. The results of the Fisher statistics and the f-k analysis show that beamforming is capable to discriminate coherent signals from noise.
For beamforming analysis, it is necessary to have array elements that are distant from the event. As the ar- ray design is fixed, it is, therefore, necessary to eliminate data from array elements in the proximity of the event. Hence only data from array elements in the far-field is used for beamforming. This approach decreases the num- ber of array elements, and therefore the signal to noise ratio is decreased as well. In this case, to improve the resolution of the applied beamforming technique, the array design needs to be modified, with all array elements situated in the far-field.

Three-component seismometers deployed on the sea bed have been used for many years. These so-called ocean bottom seismometers (OBS) are used to register both compressional and shear waves. The recorded seismic information is used to enhance our understanding of areas of interest located offshore, like hydrocarbon reservoirs, mid oceanic ridges, and continental margins. A lot of progress has been made on OBS applications. However, there are still some distinctive problems mainly related to clock drift, orientation, and coupling. We use and test \textit{OCloC}, a new method which seeks to improve the ability to detect and correct clock errors of OBSs. The method was successfully developed using an extensive dataset from a seismic network, which included many land- and ocean bottom-seismometers. One of the objectives is to understand under which conditions OCloC can be used when the dataset is relatively small. To do this, data from a network deployed in the Red Sea coastal area of the Kingdom of Saudi Arabia is used. This network includes two ocean bottom seismometers which are complemented by several land-stations. We collected all used data during five campaigns, both offshore and onshore. To analyse the data quality we designed and applied an extensive data quality control, where after the data is prepared for Ocloc through pre-processing. Both workflows are presented in a roadmap. This roadmap is made applicable for ocean bottom seismometers by including an orientation estimation, for which we used a technique based on Rayleigh wave polarization. Our quality control shows that the data from both ocean bottom seismometers show significant attenuation, primarily for short periods, which is possibly caused by a poor coupling to the loose sediments on the sea bed. The noise source illumination turns out to be highly anisotropic, with a high microseism noise source concentration in the Red Sea. This results in a poor signal to noise ratio for the interferometric surface wave responses. As a consequence, we find that the limited station deployment time and data quality interferes with the ability to obtain an accurate clock error. Nevertheless, the technique to estimate the OBS orientation turns out to be successful.