No abstract

No abstract

Ground-penetrating radar (GPR) is a standard geophysical technique 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 practice today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We develop and apply 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 the proposed 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.

No abstract

Ground-penetrating radar (GPR) is a method that can provide detailedinformation about the near subsurface in sedimentary and carbonateenvironments. Classical interpretation of GPR data (e.g., based onmanual feature selection) is often labor-intensive and limited bythe experience of the interpreter. Novel attribute-based classificationapproaches, typically used for seismic interpretation, can providefaster, more repeatable and less biased interpretations. We presenta 3D GPR data set collected across a paleokarst breccia pipe in theBillefjorden area on Spitsbergen, Svalbard. After performing advancedprocessing, we compare the results of a classical GPR interpretationto the results of an attribute-based classification. Our attributeclassification incorporates a selection of dip- and textural attributesas the input for a k-means clustering approach. Similar to the resultsof the classical interpretation, the resulting classes differentiatebetween undisturbed strata and breccias or fault zones. The classesalso reveal details inside the breccia pipe that are not discernedin the classical interpretation. Using nearby outcropping brecciapipes we infer the intra-pipe GPR facies to result from subtle differencessuch as breccia lithology, clast size, or pore-space filling.