@phdthesis{Platz2018,
  author    = {Platz, Anna},
  title     = {Novel pre-stack data confinement and selection for magnetotelluric data processing and its application to data of the Eastern Karoo Basin, South Africa},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-415087},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xx, 1131},
  year      = {2018},
  abstract  = {Magnetotellurics (MT) is a geophysical method that is able to image the electrical conductivity structure of the subsurface by recording time series of natural electromagnetic (EM) field variations. During the data processing these time series are divided into small segments and for each segment spectral values are computed which are typically averaged in a statistical manner to obtain MT transfer functions. Unfortunately, the presence of man-made EM noise sources often deteriorates a significant amount of the recorded time series resulting in disturbed transfer functions. Many advanced processing techniques, e.g. robust statistics, pre-stack data selection or remote reference, have been developed to tackle this problem. The first two techniques reduce the amount of outliers and noise in the data whereas the latter approach removes noise by using data from another MT station. However, especially in populated regions the data processing is still quite challenging even with these approaches. In this thesis, I present two novel pre-stack data confinement and selection criteria for the detection of outliers and noise affected data based on (i) a distance measure of each data segment with regard to the entire sample distribution and (ii) the evaluation of the magnetic polarisation direction of all segments. The first criterion is able to remove data points that scatter around the desired MT distribution and furthermore it can, under some circumstances, even reject complete data cluster originating from noise sources. The second criterion eliminates data points caused by a strongly polarised magnetic signal. Both criteria have been successfully applied to many stations with different noise contaminations showing that they can significantly improve the transfer function estimation. The novel criteria were used to evaluate a MT data set from the Eastern Karoo Basin in South Africa. The corresponding field experiment is part of an extensive research programme to collect information of the current e.g. geological setting in this region prior to a potential shale gas exploitation. The aim was to investigate whether a three-dimensional (3D) inversion of the newly measured data fosters a more realistic mapping of physical properties of the target horizon. For this purpose, a comprehensive 3D model was derived by using all available data. In a second step, I analysed parameters of the target horizon, e.g. its conductivity, that are proxies for physical properties such as thermal maturity and porosity.},
  language  = {en}
}
@misc{KruseKaoudiContrerasRojasetal.2020,
  author    = {Kruse, Sebastian and Kaoudi, Zoi and Contreras-Rojas, Bertty and Chawla, Sanjay and Naumann, Felix and Quian{\´e}-Ruiz, Jorge-Arnulfo},
  title     = {RHEEMix in the data jungle},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {6},
  doi       = {10.25932/publishup-51944},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-519443},
  pages     = {26},
  year      = {2020},
  abstract  = {Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform.},
  language  = {en}
}
@article{LeonardZhangKrebsetal.2020,
  author    = {L{\´e}onard, Fabien and Zhang, Zhen and Krebs, Holger and Bruno, Giovanni},
  title     = {Structural and morphological quantitative 3D characterisation of ammonium nitrate prills by X-ray computed tomography},
  series = {Materials},
  volume    = {13},
  journal   = {Materials},
  number    = {5},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1996-1944},
  doi       = {10.3390/ma13051230},
  pages     = {16},
  year      = {2020},
  abstract  = {The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually referred to as ANFO, is extensively used in the mining industry as a bulk explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the complex pore structure of the AN prills. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. Importantly, oil retention measurements (qualifying the efficiency of ANFO as explosives) correlate well with the specific surface area determined by XCT. XCT can therefore be employed non-destructively; it can accurately evaluate and characterise porosity in ammonium nitrate prills, and even predict their efficiency.},
  language  = {en}
}