@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}
}
@phdthesis{Samaras2016,
  author    = {Samaras, Stefanos},
  title     = {Microphysical retrieval of non-spherical aerosol particles using regularized inversion of multi-wavelength lidar data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-396528},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 190},
  year      = {2016},
  abstract  = {Numerous reports of relatively rapid climate changes over the past century make a clear case of the impact of aerosols and clouds, identified as sources of largest uncertainty in climate projections. Earth's radiation balance is altered by aerosols depending on their size, morphology and chemical composition. Competing effects in the atmosphere can be further studied by investigating the evolution of aerosol microphysical properties, which are the focus of the present work. The aerosol size distribution, the refractive index, and the single scattering albedo are commonly used such properties linked to aerosol type, and radiative forcing. Highly advanced lidars (light detection and ranging) have reduced aerosol monitoring and optical profiling into a routine process. Lidar data have been widely used to retrieve the size distribution through the inversion of the so-called Lorenz-Mie model (LMM). This model offers a reasonable treatment for spherically approximated particles, it no longer provides, though, a viable description for other naturally occurring arbitrarily shaped particles, such as dust particles. On the other hand, non-spherical geometries as simple as spheroids reproduce certain optical properties with enhanced accuracy. Motivated by this, we adapt the LMM to accommodate the spheroid-particle approximation introducing the notion of a two-dimensional (2D) shape-size distribution. Inverting only a few optical data points to retrieve the shape-size distribution is classified as a non-linear ill-posed problem. A brief mathematical analysis is presented which reveals the inherent tendency towards highly oscillatory solutions, explores the available options for a generalized solution through regularization methods and quantifies the ill-posedness. The latter will improve our understanding on the main cause fomenting instability in the produced solution spaces. The new approach facilitates the exploitation of additional lidar data points from depolarization measurements, associated with particle non-sphericity. However, the generalization of LMM vastly increases the complexity of the problem. The underlying theory for the calculation of the involved optical cross sections (T-matrix theory) is computationally so costly, that would limit a retrieval analysis to an unpractical point. Moreover the discretization of the model equation by a 2D collocation method, proposed in this work, involves double integrations which are further time consuming. We overcome these difficulties by using precalculated databases and a sophisticated retrieval software (SphInX: Spheroidal Inversion eXperiments) especially developed for our purposes, capable of performing multiple-dataset inversions and producing a wide range of microphysical retrieval outputs. Hybrid regularization in conjunction with minimization processes is used as a basis for our algorithms. Synthetic data retrievals are performed simulating various atmospheric scenarios in order to test the efficiency of different regularization methods. The gap in contemporary literature in providing full sets of uncertainties in a wide variety of numerical instances is of major concern here. For this, the most appropriate methods are identified through a thorough analysis on an overall-behavior basis regarding accuracy and stability. The general trend of the initial size distributions is captured in our numerical experiments and the reconstruction quality depends on data error level. Moreover, the need for more or less depolarization points is explored for the first time from the point of view of the microphysical retrieval. Finally, our approach is tested in various measurement cases giving further insight for future algorithm improvements.},
  language  = {en}
}