@phdthesis{Koyan2024,
  author    = {Koyan, Philipp},
  title     = {3D attribute analysis and classification to interpret ground-penetrating radar (GPR) data collected across sedimentary environments: Synthetic studies and field examples},
  doi       = {10.25932/publishup-63948},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-639488},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 115, A51},
  year      = {2024},
  abstract  = {Die Untersuchung des oberfl{\"a}chennahen Untergrundes erfolgt heutzutage bei Frage- stellungen aus den Bereichen des Bauwesens, der Arch{\"a}ologie oder der Geologie und Hydrologie oft mittels zerst{\"o}rungsfreier beziehungsweise zerst{\"o}rungsarmer Methoden der angewandten Geophysik. Ein Bereich, der eine immer zentralere Rolle in Forschung und Ingenieurwesen einnimmt, ist die Untersuchung von sediment{\"a}ren Umgebungen, zum Beispiel zur Charakterisierung oberfl{\"a}chennaher Grundwassersysteme. Ein in diesem Kontext h{\"a}ufig eingesetztes Verfahren ist das des Georadars (oftmals GPR - aus dem Englischen ground-penetrating radar). Dabei werden kurze elektromagnetische Impulse von einer Antenne in den Untergrund ausgesendet, welche dort wiederum an Kontrasten der elektromagnetischen Eigenschaften (wie zum Beispiel an der Grundwasseroberfl{\"a}che) reflektiert, gebrochen oder gestreut werden. Eine Empfangsantenne zeichnet diese Signale in Form derer Amplituden und Laufzeiten auf. Eine Analyse dieser aufgezeichneten Signale erm{\"o}glicht Aussagen {\"u}ber den Untergrund, beispielsweise {\"u}ber die Tiefenlage der Grundwasseroberfl{\"a}che oder die Lagerung und Charakteristika oberfl{\"a}chennaher Sedimentschichten. Dank des hohen Aufl{\"o}sungsverm{\"o}gens der GPR-Methode sowie stetiger technologischer Entwicklungen erfolgt heutzutage die Aufzeichnung von GPR- Daten immer h{\"a}ufiger in 3D. Trotz des hohen zeitlichen und technischen Aufwandes f{\"u}r die Datenakquisition und -bearbeitung werden die resultierenden 3D-Datens{\"a}tze, welche den Untergrund hochaufl{\"o}send abbilden, typischerweise von Hand interpretiert. Dies ist in der Regel ein {\"a}ußerst zeitaufwendiger Analyseschritt. Daher werden oft repr{\"a}sentative 2D-Schnitte aus dem 3D-Datensatz gew{\"a}hlt, in denen markante Reflektionsstrukuren markiert werden. Aus diesen Strukturen werden dann sich {\"a}hnelnde Bereiche im Untergrund als so genannte Radar-Fazies zusammengefasst. Die anhand von 2D-Schnitten erlangten Resultate werden dann als repr{\"a}sentativ f{\"u}r die gesamte untersuchte Fl{\"a}che angesehen. In dieser Form durchgef{\"u}hrte Interpretationen sind folglich oft unvollst{\"a}ndig sowie zudem in hohem Maße von der Expertise der Interpretierenden abh{\"a}ngig und daher in der Regel nicht reproduzierbar. Eine vielversprechende Alternative beziehungsweise Erg{\"a}nzung zur manuellen In- terpretation ist die Verwendung von so genannten GPR-Attributen. Dabei werden nicht die aufgezeichneten Daten selbst, sondern daraus abgeleitete Gr{\"o}ßen, welche die markanten Reflexionsstrukturen in 3D charakterisieren, zur Interpretation herangezogen. In dieser Arbeit wird anhand verschiedener Feld- und Modelldatens{\"a}tze untersucht, welche Attribute sich daf{\"u}r insbesondere eignen. Zudem zeigt diese Arbeit, wie ausgew{\"a}hlte Attribute mittels spezieller Bearbeitungs- und Klassifizierungsmethoden zur Erstellung von 3D-Faziesmodellen genutzt werden k{\"o}nnen. Dank der M{\"o}glichkeit der Erstellung so genannter attributbasierter 3D-GPR-Faziesmodelle k{\"o}nnen zuk{\"u}nftige Interpretationen zu gewissen Teilen automatisiert und somit effizienter durchgef{\"u}hrt werden. Weiterhin beschreiben die so erhaltenen Resultate den untersuchten Untergrund in reproduzierbarer Art und Weise sowie umf{\"a}nglicher als es bisher mittels manueller Interpretationsmethoden typischerweise m{\"o}glich war.},
  language  = {en}
}
@article{KoyanTronickeAllroggen2021,
  author    = {Koyan, Philipp and Tronicke, Jens and Allroggen, Niklas},
  title     = {3D ground-penetrating radar attributes to generate classified facies models},
  series = {Geophysics},
  volume    = {86},
  journal   = {Geophysics},
  number    = {6},
  publisher = {Society of Exploration Geophysicists},
  address   = {Tulsa},
  issn      = {0016-8033},
  doi       = {10.1190/GEO2021-0204.1},
  pages     = {B335 -- B347},
  year      = {2021},
  abstract  = {Ground-penetrating radar (GPR) is a standard geophysical technique used to image near-surface structures in sedimentary environments. In such environments, GPR data acquisition and processing are increasingly following 3D strategies. However, the processed GPR data volumes are typically still interpreted using selected 2D slices and manual concepts such as GPR facies analyses. In seismic volume interpretation, the application of (semi-)automated and reproducible approaches such as 3D attribute analyses as well as the production of attribute-based facies models are common practices today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We have developed and applied a workflow to produce 3D attribute-based GPR facies models comprising the dominant sedimentary reflection patterns in a GPR volume, which images complex sandy structures on the dune island of Spiekeroog (Northern Germany). After presenting our field site and details regarding our data acquisition and processing, we calculate and filter 3D texture attributes to generate a database comprising the dominant texture features of our GPR data. Then, we perform a dimensionality reduction of this database to obtain meta texture attributes, which we analyze and integrate using composite imaging and (also considering additional geometric information) fuzzy c-means cluster analysis resulting in a classified GPR facies model. Considering our facies model and a corresponding GPR facies chart, we interpret our GPR data set in terms of near-surface sedimentary units, the corresponding depositional environments, and the recent formation history at our field site. Thus, we demonstrate the potential of our workflow, which represents a novel and clear strategy to perform a more objective and consistent interpretation of 3D GPR data collected across different sedimentary environments.},
  language  = {en}
}
@misc{AngermannJackischAllroggenetal.2017,
  author    = {Angermann, Lisa and Jackisch, Conrad and Allroggen, Niklas and Sprenger, Matthias and Zehe, Erwin and Tronicke, Jens and Weiler, Markus and Blume, Theresa},
  title     = {Form and function in hillslope hydrology},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {658},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41916},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419161},
  pages     = {22},
  year      = {2017},
  abstract  = {The phrase form and function was established in architecture and biology and refers to the idea that form and functionality are closely correlated, influence each other, and co-evolve. We suggest transferring this idea to hydrological systems to separate and analyze their two main characteristics: their form, which is equivalent to the spatial structure and static properties, and their function, equivalent to internal responses and hydrological behavior. While this approach is not particularly new to hydrological field research, we want to employ this concept to explicitly pursue the question of what information is most advantageous to understand a hydrological system. We applied this concept to subsurface flow within a hillslope, with a methodological focus on function: we conducted observations during a natural storm event and followed this with a hillslope-scale irrigation experiment. The results are used to infer hydrological processes of the monitored system. Based on these findings, the explanatory power and conclusiveness of the data are discussed. The measurements included basic hydrological monitoring methods, like piezometers, soil moisture, and discharge measurements. These were accompanied by isotope sampling and a novel application of 2-D time-lapse GPR (ground-penetrating radar). The main finding regarding the processes in the hillslope was that preferential flow paths were established quickly, despite unsaturated conditions. These flow paths also caused a detectable signal in the catchment response following a natural rainfall event, showing that these processes are relevant also at the catchment scale. Thus, we conclude that response observations (dynamics and patterns, i.e., indicators of function) were well suited to describing processes at the observational scale. Especially the use of 2-D time-lapse GPR measurements, providing detailed subsurface response patterns, as well as the combination of stream-centered and hillslope-centered approaches, allowed us to link processes and put them in a larger context. Transfer to other scales beyond observational scale and generalizations, however, rely on the knowledge of structures (form) and remain speculative. The complementary approach with a methodological focus on form (i.e., structure exploration) is presented and discussed in the companion paper by Jackisch et al. (2017).},
  language  = {en}
}
@article{FarinottiKingAlbrechtetal.2014,
  author    = {Farinotti, Daniel and King, Edward C. and Albrecht, Anika and Huss, Matthias and Gudmundsson, Gudmundur Hilmar},
  title     = {The bedrock topography of Starbuck Glacier, Antarctic Peninsula, as determined by radio-echo soundings and flow modeling},
  series = {Annals of glaciology},
  volume    = {55},
  journal   = {Annals of glaciology},
  number    = {67},
  publisher = {International Glaciological Society},
  address   = {Cambridge},
  issn      = {0260-3055},
  doi       = {10.3189/2014AoG67A025},
  pages     = {22 -- 28},
  year      = {2014},
  language  = {en}
}