TY - JOUR A1 - Allroggen, Niklas A1 - Heincke, Bjorn H. A1 - Koyan, Philipp A1 - Wheeler, Walter A1 - Ronning, Jan S. T1 - 3D ground-penetrating radar attribute classification BT - a case study from a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard JF - Geophysics N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1190/GEO2021-0651.1 SN - 0016-8033 SN - 1942-2156 VL - 87 IS - 4 SP - WB19 EP - WB30 PB - Society of Exploration Geophysicists CY - Tulsa ER - TY - JOUR A1 - Koyan, Philipp A1 - Tronicke, Jens T1 - 3D modeling of ground-penetrating radar data across a realistic sedimentary model JF - 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 N2 - 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. KW - Applied geophysics KW - Ground-penetrating radar KW - 3D modeling Y1 - 2020 U6 - https://doi.org/10.1016/j.cageo.2020.104422 SN - 0098-3004 SN - 1873-7803 VL - 137 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Koyan, Philipp A1 - Tronicke, Jens A1 - Allroggen, Niklas T1 - 3D ground-penetrating radar attributes to generate classified facies models BT - a case study from a dune island JF - Geophysics N2 - 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. KW - ground-penetrating radar KW - attributes KW - interpretation KW - sedimentology Y1 - 2021 U6 - https://doi.org/10.1190/GEO2021-0204.1 SN - 0016-8033 SN - 1942-2156 VL - 86 IS - 6 SP - B335 EP - B347 PB - Society of Exploration Geophysicists CY - Tulsa ER - TY - THES A1 - Koyan, Philipp T1 - 3D attribute analysis and classification to interpret ground-penetrating radar (GPR) data collected across sedimentary environments: Synthetic studies and field examples T1 - 3D Attributanalyse und -klassifizierung zur Interpretation von Georadar-Daten in sedimentären Umgebungen: Synthetische Studien und Feldbeispiele N2 - Die Untersuchung des oberflächennahen Untergrundes erfolgt heutzutage bei Frage- stellungen aus den Bereichen des Bauwesens, der Archäologie oder der Geologie und Hydrologie oft mittels zerstörungsfreier beziehungsweise zerstörungsarmer Methoden der angewandten Geophysik. Ein Bereich, der eine immer zentralere Rolle in Forschung und Ingenieurwesen einnimmt, ist die Untersuchung von sedimentären Umgebungen, zum Beispiel zur Charakterisierung oberflächennaher Grundwassersysteme. Ein in diesem Kontext häufig 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 Grundwasseroberfläche) reflektiert, gebrochen oder gestreut werden. Eine Empfangsantenne zeichnet diese Signale in Form derer Amplituden und Laufzeiten auf. Eine Analyse dieser aufgezeichneten Signale ermöglicht Aussagen über den Untergrund, beispielsweise über die Tiefenlage der Grundwasseroberfläche oder die Lagerung und Charakteristika oberflächennaher Sedimentschichten. Dank des hohen Auflösungsvermögens der GPR-Methode sowie stetiger technologischer Entwicklungen erfolgt heutzutage die Aufzeichnung von GPR- Daten immer häufiger in 3D. Trotz des hohen zeitlichen und technischen Aufwandes für die Datenakquisition und -bearbeitung werden die resultierenden 3D-Datensätze, welche den Untergrund hochauflösend abbilden, typischerweise von Hand interpretiert. Dies ist in der Regel ein äußerst zeitaufwendiger Analyseschritt. Daher werden oft repräsentative 2D-Schnitte aus dem 3D-Datensatz gewählt, in denen markante Reflektionsstrukuren markiert werden. Aus diesen Strukturen werden dann sich ähnelnde Bereiche im Untergrund als so genannte Radar-Fazies zusammengefasst. Die anhand von 2D-Schnitten erlangten Resultate werden dann als repräsentativ für die gesamte untersuchte Fläche angesehen. In dieser Form durchgeführte Interpretationen sind folglich oft unvollständig sowie zudem in hohem Maße von der Expertise der Interpretierenden abhängig und daher in der Regel nicht reproduzierbar. Eine vielversprechende Alternative beziehungsweise Ergänzung zur manuellen In- terpretation ist die Verwendung von so genannten GPR-Attributen. Dabei werden nicht die aufgezeichneten Daten selbst, sondern daraus abgeleitete Größen, welche die markanten Reflexionsstrukturen in 3D charakterisieren, zur Interpretation herangezogen. In dieser Arbeit wird anhand verschiedener Feld- und Modelldatensätze untersucht, welche Attribute sich dafür insbesondere eignen. Zudem zeigt diese Arbeit, wie ausgewählte Attribute mittels spezieller Bearbeitungs- und Klassifizierungsmethoden zur Erstellung von 3D-Faziesmodellen genutzt werden können. Dank der Möglichkeit der Erstellung so genannter attributbasierter 3D-GPR-Faziesmodelle können zukünftige Interpretationen zu gewissen Teilen automatisiert und somit effizienter durchgeführt werden. Weiterhin beschreiben die so erhaltenen Resultate den untersuchten Untergrund in reproduzierbarer Art und Weise sowie umfänglicher als es bisher mittels manueller Interpretationsmethoden typischerweise möglich war. N2 - Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today. Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible. A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods. KW - ground-penetrating radar KW - sedimentary environments KW - 3D KW - applied geophysics KW - near-surface geophysics KW - Georadar KW - sedimentäre Systeme KW - angewandte Geophysik KW - oberflächennahe Geophysik KW - Attribute KW - attributes KW - geophysics KW - Geophysik Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-639488 ER -