@article{AllroggenHeinckeKoyanetal.2022, author = {Allroggen, Niklas and Heincke, Bjorn H. and Koyan, Philipp and Wheeler, Walter and Ronning, Jan S.}, title = {3D ground-penetrating radar attribute classification}, series = {Geophysics}, volume = {87}, journal = {Geophysics}, number = {4}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2021-0651.1}, pages = {WB19 -- WB30}, year = {2022}, abstract = {Ground-penetrating radar (GPR) is a method that can provide detailed information about the near subsurface in sedimentary and carbonate environments. The classical interpretation of GPR data (e.g., based on manual feature selection) often is labor-intensive and limited by the experience of the intercally used for seismic interpretation, can provide faster, more repeatable, and less biased interpretations. We have recorded a 3D GPD data set collected across a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard. After performing advanced processing, we compare the results of a classical GPR interpretation to the results of an attribute-based classification. Our attribute classification incorporates a selection of dip and textural attributes as the input for a k-means clustering approach. Similar to the results of the classical interpretation, the resulting classes differentiate between undisturbed strata and breccias or fault zones. The classes also reveal details inside the breccia pipe that are not discerned in the classical fer that the intrapipe GPR facies result from subtle differences, such as breccia lithology, clast size, or pore-space filling.}, language = {en} } @article{KoyanTronicke2020, author = {Koyan, Philipp and Tronicke, Jens}, title = {3D modeling of ground-penetrating radar data across a realistic sedimentary model}, series = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology}, volume = {137}, journal = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0098-3004}, doi = {10.1016/j.cageo.2020.104422}, pages = {9}, year = {2020}, abstract = {Ground-penetrating radar (GPR) is an established geophysical tool to explore a wide range of near-surface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a time-efficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now.
We use a published hydrofacies data set of an aquifer-analog study within fluvio-glacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming fresh-water saturated sediments, we generate synthetic 3D GPR data across this model using novel GPU-acceleration included in the open-source software gprMax. We present a numerical approach to examine 3D wave-propagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other near-surface geophysical techniques.}, 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} } @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} }