@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}
}
@phdthesis{Boeniger2010,
  author    = {B{\"o}niger, Urs},
  title     = {Attributes and their potential to analyze and interpret 3D GPR data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-50124},
  school      = {Universit{\"a}t Potsdam},
  year      = {2010},
  abstract  = {Based on technological advances made within the past decades, ground-penetrating radar (GPR) has become a well-established, non-destructive subsurface imaging technique. Catalyzed by recent demands for high-resolution, near-surface imaging (e.g., the detection of unexploded ordnances and subsurface utilities, or hydrological investigations), the quality of today's GPR-based, near-surface images has significantly matured. At the same time, the analysis of oil and gas related reflection seismic data sets has experienced significant advances. Considering the sensitivity of attribute analysis with respect to data positioning in general, and multi-trace attributes in particular, trace positioning accuracy is of major importance for the success of attribute-based analysis flows. Therefore, to study the feasibility of GPR-based attribute analyses, I first developed and evaluated a real-time GPR surveying setup based on a modern tracking total station (TTS). The combination of current GPR systems capability of fusing global positioning system (GPS) and geophysical data in real-time, the ability of modern TTS systems to generate a GPS-like positional output and wireless data transmission using radio modems results in a flexible and robust surveying setup. To elaborate the feasibility of this setup, I studied the major limitations of such an approach: system cross-talk and data delays known as latencies. Experimental studies have shown that when a minimal distance of ~5 m between the GPR and the TTS system is considered, the signal-to-noise ratio of the acquired GPR data using radio communication equals the one without radio communication. To address the limitations imposed by system latencies, inherent to all real-time data fusion approaches, I developed a novel correction (calibration) strategy to assess the gross system latency and to correct for it. This resulted in the centimeter trace accuracy required by high-frequency and/or three-dimensional (3D) GPR surveys. Having introduced this flexible high-precision surveying setup, I successfully demonstrated the application of attribute-based processing to GPR specific problems, which may differ significantly from the geological ones typically addressed by the oil and gas industry using seismic data. In this thesis, I concentrated on archaeological and subsurface utility problems, as they represent typical near-surface geophysical targets. Enhancing 3D archaeological GPR data sets using a dip-steered filtering approach, followed by calculation of coherency and similarity, allowed me to conduct subsurface interpretations far beyond those obtained by classical time-slice analyses. I could show that the incorporation of additional data sets (magnetic and topographic) and attributes derived from these data sets can further improve the interpretation. In a case study, such an approach revealed the complementary nature of the individual data sets and, for example, allowed conclusions about the source location of magnetic anomalies by concurrently analyzing GPR time/depth slices to be made. In addition to archaeological targets, subsurface utility detection and characterization is a steadily growing field of application for GPR. I developed a novel attribute called depolarization. Incorporation of geometrical and physical feature characteristics into the depolarization attribute allowed me to display the observed polarization phenomena efficiently. Geometrical enhancement makes use of an improved symmetry extraction algorithm based on Laplacian high-boosting, followed by a phase-based symmetry calculation using a two-dimensional (2D) log-Gabor filterbank decomposition of the data volume. To extract the physical information from the dual-component data set, I employed a sliding-window principle component analysis. The combination of the geometrically derived feature angle and the physically derived polarization angle allowed me to enhance the polarization characteristics of subsurface features. Ground-truth information obtained by excavations confirmed this interpretation. In the future, inclusion of cross-polarized antennae configurations into the processing scheme may further improve the quality of the depolarization attribute. In addition to polarization phenomena, the time-dependent frequency evolution of GPR signals might hold further information on the subsurface architecture and/or material properties. High-resolution, sparsity promoting decomposition approaches have recently had a significant impact on the image and signal processing community. In this thesis, I introduced a modified tree-based matching pursuit approach. Based on different synthetic examples, I showed that the modified tree-based pursuit approach clearly outperforms other commonly used time-frequency decomposition approaches with respect to both time and frequency resolutions. Apart from the investigation of tuning effects in GPR data, I also demonstrated the potential of high-resolution sparse decompositions for advanced data processing. Frequency modulation of individual atoms themselves allows to efficiently correct frequency attenuation effects and improve resolution based on shifting the average frequency level. GPR-based attribute analysis is still in its infancy. Considering the growing widespread realization of 3D GPR studies there will certainly be an increasing demand towards improved subsurface interpretations in the future. Similar to the assessment of quantitative reservoir properties through the combination of 3D seismic attribute volumes with sparse well-log information, parameter estimation in a combined manner represents another step in emphasizing the potential of attribute-driven GPR data analyses.},
  language  = {en}
}