GPR pattern recognition of shallow subsurface air voids

Countless subsurface voids in urban areas of cities threaten people's lives and property. A workflow for automatically identifying subsurface voids from ground penetrating radar (GPR) data was developed in this study. The workflow consists of 3 stages: locating voids automatically from C-scans, then verifying voids from corresponding B-scans, and finally making judgements based upon the previous 2 sets of results. This study adopted 2 (Lai et al., 2016) approaches: approach 1 quantified the GPR response of air voids using forward modelling, while approach 2 used workflow prototyping and validation with inverse modelling. Forward simulations indicated that different ratios of void size to GPR signal footprint could result in a variety of patterns in B-scans: they can be hyperbolas, cross patterns, bowl shaped patterns and reverberations. With a database of void patterns of both C-scans and B-scans established, in approach 2 the workflow uses a pyramid pattern recognition method – with pixel value or gradient being used for feature identification – to search automatically for air-filled void responses in GPR data. The workflow was tested using 2 laboratory and field experiments and the results were promising. The constraint values proposed by the 2 experiments were validated with another site experiment. Given the huge workload involved in city-scale subsurface health inspections, a standardized workflow can help improve efficiency and effectiveness of subsurface void identification.