@phdthesis{Hendriyana2017,
  author    = {Hendriyana, Andri},
  title     = {Detection and Kirchhoff-type migration of seismic events by use of a new characteristic function},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-398879},
  school      = {Universit{\"a}t Potsdam},
  pages     = {v, 139},
  year      = {2017},
  abstract  = {The classical method of seismic event localization is based on the picking of body wave arrivals, ray tracing and inversion of travel time data. Travel time picks with small uncertainties are required to produce reliable and accurate results with this kind of source localization. Hence recordings, with a low Signal-to-Noise Ratio (SNR) cannot be used in a travel time based inversion. Low SNR can be related with weak signals from distant and/or low magnitude sources as well as with a high level of ambient noise. Diffraction stacking is considered as an alternative seismic event localization method that enables also the processing of low SNR recordings by mean of stacking the amplitudes of seismograms along a travel time function. The location of seismic event and its origin time are determined based on the highest stacked amplitudes (coherency) of the image function. The method promotes an automatic processing since it does not need travel time picks as input data. However, applying diffraction stacking may require longer computation times if only limited computer resources are used. Furthermore, a simple diffraction stacking of recorded amplitudes could possibly fail to locate the seismic sources if the focal mechanism leads to complex radiation patterns which typically holds for both natural and induced seismicity. In my PhD project, I have developed a new work flow for the localization of seismic events which is based on a diffraction stacking approach. A parallelized code was implemented for the calculation of travel time tables and for the determination of an image function to reduce computation time. In order to address the effects from complex source radiation patterns, I also suggest to compute diffraction stacking from a characteristic function (CF) instead of stacking the original wave form data. A new CF, which is called in the following mAIC (modified from Akaike Information Criterion) is proposed. I demonstrate that, the performance of the mAIC does not depend on the chosen length of the analyzed time window and that both P- and S-wave onsets can be detected accurately. To avoid cross-talk between P- and S-waves due to inaccurate velocity models, I separate the P- and S-waves from the mAIC function by making use of polarization attributes. Then, eventually the final image function is represented by the largest eigenvalue as a result of the covariance analysis between P- and S-image functions. Before applying diffraction stacking, I also apply seismogram denoising by using Otsu thresholding in the time-frequency domain. Results from synthetic experiments show that the proposed diffraction stacking provides reliable results even from seismograms with low SNR=1. Tests with different presentations of the synthetic seismograms (displacement, velocity, and acceleration) shown that, acceleration seismograms deliver better results in case of high SNR, whereas displacement seismograms provide more accurate results in case of low SNR recordings. In another test, different measures (maximum amplitude, other statistical parameters) were used to determine the source location in the final image function. I found that the statistical approach is the preferred method particularly for low SNR. The work flow of my diffraction stacking method was finally applied to local earthquake data from Sumatra, Indonesia. Recordings from a temporary network of 42 stations deployed for 9 months around the Tarutung pull-apart Basin were analyzed. The seismic event locations resulting from the diffraction stacking method align along a segment of the Sumatran Fault. A more complex distribution of seismicity is imaged within and around the Tarutung Basin. Two lineaments striking N-S were found in the middle of the Tarutung Basin which support independent results from structural geology. These features are interpreted as opening fractures due to local extension. A cluster of seismic events repeatedly occurred in short time which might be related to fluid drainage since two hot springs are observed at the surface near to this cluster.},
  language  = {en}
}
@phdthesis{Lontsi2016,
  author    = {Lontsi, Agostiny Marrios},
  title     = {1D shallow sedimentary subsurface imaging using ambient noise and active seismic data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-103807},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 119},
  year      = {2016},
  abstract  = {The Earth's shallow subsurface with sedimentary cover acts as a waveguide to any incoming wavefield. Within the framework of my thesis, I focused on the characterization of this shallow subsurface within tens to few hundreds of meters of sediment cover. I imaged the seismic 1D shear wave velocity (and possibly the 1D compressional wave velocity). This information is not only required for any seismic risk assessment, geotechnical engineering or microzonation activities, but also for exploration and global seismology where site effects are often neglected in seismic waveform modeling. First, the conventional frequency-wavenumber (f - k) technique is used to derive the dispersion characteristic of the propagating surface waves recorded using distinct arrays of seismometers in 1D and 2D configurations. Further, the cross-correlation technique is applied to seismic array data to estimate the Green's function between receivers pairs combination assuming one is the source and the other the receiver. With the consideration of a 1D media, the estimated cross-correlation Green's functions are sorted with interstation distance in a virtual 1D active seismic experiment. The f - k technique is then used to estimate the dispersion curves. This integrated analysis is important for the interpretation of a large bandwidth of the phase velocity dispersion curves and therefore improving the resolution of the estimated 1D Vs profile. Second, the new theoretical approach based on the Diffuse Field Assumption (DFA) is used for the interpretation of the observed microtremors H/V spectral ratio. The theory is further extended in this research work to include not only the interpretation of the H/V measured at the surface, but also the H/V measured at depths and in marine environments. A modeling and inversion of synthetic H/V spectral ratio curves on simple predefined geological structures shows an almost perfect recovery of the model parameters (mainly Vs and to a lesser extent Vp). These results are obtained after information from a receiver at depth has been considered in the inversion. Finally, the Rayleigh wave phase velocity information, estimated from array data, and the H/V(z, f) spectral ratio, estimated from a single station data, are combined and inverted for the velocity profile information. Obtained results indicate an improved depth resolution in comparison to estimations using the phase velocity dispersion curves only. The overall estimated sediment thickness is comparable to estimations obtained by inverting the full micortremor H/V spectral ratio.},
  language  = {en}
}