@phdthesis{Shirzaei2010,
  author    = {Shirzaei, Manoochehr},
  title     = {Crustal deformation source monitoring using advanced InSAR time series and time dependent inverse modeling},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-50774},
  school      = {Universit{\"a}t Potsdam},
  year      = {2010},
  abstract  = {Crustal deformation can be the result of volcanic and tectonic activity such as fault dislocation and magma intrusion. The crustal deformation may precede and/or succeed the earthquake occurrence and eruption. Mitigating the associated hazard, continuous monitoring of the crustal deformation accordingly has become an important task for geo-observatories and fast response systems. Due to highly non-linear behavior of the crustal deformation fields in time and space, which are not always measurable using conventional geodetic methods (e.g., Leveling), innovative techniques of monitoring and analysis are required. In this thesis I describe novel methods to improve the ability for precise and accurate mapping the spatiotemporal surface deformation field using multi acquisitions of satellite radar data. Furthermore, to better understand the source of such spatiotemporal deformation fields, I present novel static and time dependent model inversion approaches. Almost any interferograms include areas where the signal decorrelates and is distorted by atmospheric delay. In this thesis I detail new analysis methods to reduce the limitations of conventional InSAR, by combining the benefits of advanced InSAR methods such as the permanent scatterer InSAR (PSI) and the small baseline subsets (SBAS) with a wavelet based data filtering scheme. This novel InSAR time series methodology is applied, for instance, to monitor the non-linear deformation processes at Hawaii Island. The radar phase change at Hawaii is found to be due to intrusions, eruptions, earthquakes and flank movement processes and superimposed by significant environmental artifacts (e.g., atmospheric). The deformation field, I obtained using the new InSAR analysis method, is in good agreement with continuous GPS data. This provides an accurate spatiotemporal deformation field at Hawaii, which allows time dependent source modeling. Conventional source modeling methods usually deal with static deformation field, while retrieving the dynamics of the source requires more sophisticated time dependent optimization approaches. This problem I address by combining Monte Carlo based optimization approaches with a Kalman Filter, which provides the model parameters of the deformation source consistent in time. I found there are numerous deformation sources at Hawaii Island which are spatiotemporally interacting, such as volcano inflation is associated to changes in the rifting behavior, and temporally linked to silent earthquakes. I applied these new methods to other tectonic and volcanic terrains, most of which revealing the importance of associated or coupled deformation sources. The findings are 1) the relation between deep and shallow hydrothermal and magmatic sources underneath the Campi Flegrei volcano, 2) gravity-driven deformation at Damavand volcano, 3) fault interaction associated with the 2010 Haiti earthquake, 4) independent block wise flank motion at the Hilina Fault system, Kilauea, and 5) interaction between salt diapir and the 2005 Qeshm earthquake in southern Iran. This thesis, written in cumulative form including 9 manuscripts published or under review in peer reviewed journals, improves the techniques for InSAR time series analysis and source modeling and shows the mutual dependence between adjacent deformation sources. These findings allow more realistic estimation of the hazard associated with complex volcanic and tectonic systems.},
  language  = {en}
}
@phdthesis{Zoeller1999,
  author    = {Z{\"o}ller, Gert},
  title     = {Analyse raumzeitlicher Muster in Erdbebendaten},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0000122},
  school      = {Universit{\"a}t Potsdam},
  year      = {1999},
  abstract  = {Die vorliegende Arbeit besch{\"a}ftigt sich mit der Charakterisierung von Seismizit{\"a}t anhand von Erdbebenkatalogen. Es werden neue Verfahren der Datenanalyse entwickelt, die Aufschluss dar{\"u}ber geben sollen, ob der seismischen Dynamik ein stochastischer oder ein deterministischer Prozess zugrunde liegt und was daraus f{\"u}r die Vorhersagbarkeit starker Erdbeben folgt. Es wird gezeigt, dass seismisch aktive Regionen h{\"a}ufig durch nichtlinearen Determinismus gekennzeichent sind. Dies schließt zumindest die M{\"o}glichkeit einer Kurzzeitvorhersage ein. Das Auftreten seismischer Ruhe wird h{\"a}ufig als Vorl{\"a}uferphaenomen f{\"u}r starke Erdbeben gedeutet. Es wird eine neue Methode pr{\"a}sentiert, die eine systematische raumzeitliche Kartierung seismischer Ruhephasen erm{\"o}glicht. Die statistische Signifikanz wird mit Hilfe des Konzeptes der Ersatzdaten bestimmt. Als Resultat erh{\"a}lt man deutliche Korrelationen zwischen seismischen Ruheperioden und starken Erdbeben. Gleichwohl ist die Signifikanz daf{\"u}r nicht hoch genug, um eine Vorhersage im Sinne einer Aussage {\"u}ber den Ort, die Zeit und die St{\"a}rke eines zu erwartenden Hauptbebens zu erm{\"o}glichen.},
  language  = {en}
}