@phdthesis{Kriegerowski2019,
  author    = {Kriegerowski, Marius},
  title     = {Development of waveform-based, automatic analysis tools for the spatio-temporal characterization of massive earthquake clusters and swarms},
  doi       = {10.25932/publishup-44404},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-444040},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xv, 83},
  year      = {2019},
  abstract  = {Earthquake swarms are characterized by large numbers of events occurring in a short period of time within a confined source volume and without significant mainshock aftershock pattern as opposed to tectonic sequences. Intraplate swarms in the absence of active volcanism usually occur in continental rifts as for example in the Eger Rift zone in North West Bohemia, Czech Republic. A common hypothesis links event triggering to pressurized fluids. However, the exact causal chain is often poorly understood since the underlying geotectonic processes are slow compared to tectonic sequences. The high event rate during active periods challenges standard seismological routines as these are often designed for single events and therefore costly in terms of human resources when working with phase picks or computationally costly when exploiting full waveforms. This methodological thesis develops new approaches to analyze earthquake swarm seismicity as well as the underlying seismogenic volume. It focuses on the region of North West (NW) Bohemia, a well studied, well monitored earthquake swarm region. In this work I develop and test an innovative approach to detect and locate earthquakes using deep convolutional neural networks. This technology offers great potential as it allows to efficiently process large amounts of data which becomes increasingly important given that seismological data storage grows at increasing pace. The proposed deep neural network trained on NW Bohemian earthquake swarm records is able to locate 1000 events in less than 1 second using full waveforms while approaching precision of double difference relocated catalogs. A further technological novelty is that the trained filters of the deep neural network's first layer can be repurposed to function as a pattern matching event detector without additional training on noise datasets. For further methodological development and benchmarking, I present a new toolbox to generate realistic earthquake cluster catalogs as well as synthetic full waveforms of those clusters in an automated fashion. The input is parameterized using constraints on source volume geometry, nucleation and frequency-magnitude relations. It harnesses recorded noise to produce highly realistic synthetic data for benchmarking and development. This tool is used to study and assess detection performance in terms of magnitude of completeness Mc of a full waveform detector applied to synthetic data of a hydrofracturing experiment at the Wysin site, Poland. Finally, I present and demonstrate a novel approach to overcome the masking effects of wave propagation between earthquake and stations and to determine source volume attenuation directly in the source volume where clustered earthquakes occur. The new event couple spectral ratio approach exploits high frequency spectral slopes of two events sharing the greater part of their rays. Synthetic tests based on the toolbox mentioned before show that this method is able to infer seismic wave attenuation within the source volume at high spatial resolution. Furthermore, it is independent from the distance towards a station as well as the complexity of the attenuation and velocity structure outside of the source volume of swarms. The application to recordings of the NW Bohemian earthquake swarm shows increased P phase attenuation within the source volume (Qp < 100) based on results at a station located close to the village Luby (LBC). The recordings of a station located in epicentral proximity, close to Nov{\´y} Kostel (NKC), show a relatively high complexity indicating that waves arriving at that station experience more scattering than signals recorded at other stations. The high level of complexity destabilizes the inversion. Therefore, the Q estimate at NKC is not reliable and an independent proof of the high attenuation finding given the geometrical and frequency constraints is still to be done. However, a high attenuation in the source volume of NW Bohemian swarms has been postulated before in relation to an expected, highly damaged zone bearing CO 2 at high pressure. The methods developed in the course of this thesis yield the potential to improve our understanding regarding the role of fluids and gases in intraplate event clustering.},
  language  = {en}
}
@phdthesis{Petersen2021,
  author    = {Petersen, Gesa Maria},
  title     = {Source studies of small earthquakes in the AlpArray: CMT inversion, seismo-tectonic analysis and methodological developments},
  doi       = {10.25932/publishup-52563},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-525635},
  school      = {Universit{\"a}t Potsdam},
  pages     = {151},
  year      = {2021},
  abstract  = {Centroid moment tensor inversion can provide insight into ongoing tectonic processes and active faults. In the Alpine mountains (central Europe), challenges result from low signal-to-noise ratios of earthquakes with small to moderate magnitudes and complex wave propagation effects through the heterogeneous crustal structure of the mountain belt. In this thesis, I make use of the temporary installation of the dense AlpArray seismic network (AASN) to establish a work flow to study seismic source processes and enhance the knowledge of the Alpine seismicity. The cumulative thesis comprises four publications on the topics of large seismic networks, seismic source processes in the Alps, their link to tectonics and stress field, and the inclusion of small magnitude earthquakes into studies of active faults. Dealing with hundreds of stations of the dense AASN requires the automated assessment of data and metadata quality. I developed the open source toolbox AutoStatsQ to perform an automated data quality control. Its first application to the AlpArray seismic network has revealed significant errors of amplitude gains and sensor orientations. A second application of the orientation test to the Turkish KOERI network, based on Rayleigh wave polarization, further illustrated the potential in comparison to a P wave polarization method. Taking advantage of the gain and orientation results of the AASN, I tested different inversion settings and input data types to approach the specific challenges of centroid moment tensor (CMT) inversions in the Alps. A comparative study was carried out to define the best fitting procedures. The application to 4 years of seismicity in the Alps (2016-2019) substantially enhanced the amount of moment tensor solutions in the region. We provide a list of moment tensors solutions down to magnitude Mw 3.1. Spatial patterns of typical focal mechanisms were analyzed in the seismotectonic context, by comparing them to long-term seismicity, historical earthquakes and observations of strain rates. Additionally, we use our MT solutions to investigate stress regimes and orientations along the Alpine chain. Finally, I addressed the challenge of including smaller magnitude events into the study of active faults and source processes. The open-source toolbox Clusty was developed for the clustering of earthquakes based on waveforms recorded across a network of seismic stations. The similarity of waveforms reflects both, the location and the similarity of source mechanisms. Therefore the clustering bears the opportunity to identify earthquakes of similar faulting styles, even when centroid moment tensor inversion is not possible due to low signal-to-noise ratios of surface waves or oversimplified velocity models. The toolbox is described through an application to the Zakynthos 2018 aftershock sequence and I subsequently discuss its potential application to weak earthquakes (Mw<3.1) in the Alps.},
  language  = {en}
}