From 3D GPR facies and structures toward 3D petrophysical parameter models

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Ground-penetrating radar (GPR) is an established near-surface geophysical tool, which is increasingly employed in variety of applications. Although we see steady methodological progress for improved GPR data acquisition and processing, the concepts and techniques typically employed for interpreting processed GPR reflection images have not significantly changed within the past decades. When exploring near-surface sedimentary environments, 2D and 3D GPR reflection surveying is routinely used to map and image subsurface architecture as needed, for example, to characterize depositional environments or to aid hydrogeological investigations. Even today, the interpretation of such GPR data sets largely relies on manual strategies such as reflector picking (to outline horizons) and delineating units of characteristic reflection patterns (to identify different GPR facies). To interpret GPR data in a more objective, efficient, and also more automated fashion, we have considered attribute-based interpretation strategies and developed, in the preceding project, a workflow for a largely automated delineation of GPR facies. This workflow relies on generating an attribute database consisting of various geometric and texture attributes. After attribute filtering and statistical analyses (e.g., to reduce redundancies information), we derive 3D facies images and classified facies models from our database. As demonstrated by synthetic and field data examples, these facies models outline characteristic structural units being helpful to develop a geological understanding of the buried subsurface. However, for many (e.g., hydrogeological or geotechnical) applications structural facies models are often not sufficient because a more quantitative understanding of subsurface characteristics and processes is needed. For example, in hydrogeology detailed models of the relevant subsurface structures and the governing material properties (i.e., porosity and hydraulic conductivity) are needed to develop a comprehensive understanding of groundwater flow and transport processes. Thus, we aim at extending our so far developed interpretation strategy by including additional point-based information and data as provided, for example, by independent direct-push soundings and borehole logs. We propose different ideas for incorporating such point data into the workflow including approaches based on advanced statistics and machine learning. These approaches will be compared to each other and evaluated using different synthetic and field data examples. Thus, the major objectives of this follow-up project are, firstly, to identify GPR attributes suitable for establishing links to different point-based petrophysical parameters and, secondly, to identify suitable strategies and methods to derive meaningful and reliable 3D models comprising structural and petrophysical characteristics of near-surface sedimentary environments.