oa Time-Lapse Imaging of Dynamic Systems using Multi-offset GPR Reflection Data

Ground-penetrating radar is widely recognized as a powerful tool for mapping the subsurface to image structures and locate targets. However, velocity variations resulting from heterogeneity in soil texture and water content can greatly degrade the coherency of radar responses. This issue is particularly problematic in dynamic environments where the background environment may change over time, e.g., as a result of rainwater infiltration. to address this problem we investigate the radar signature of reflectors embedded in homogenous and layered soils during the course of a simulated infiltration event. the data are collected using multiple antenna offsets to allow for evaluation of traveltime and amplitude changes with offset. the result of this survey design is a rich data cube with dimensions of GPR traveltime, experiment time, and antenna offset. We demonstrate the value of this data by performing normal moveout velocity analysis for arrivals obtained over the course of the experiment. We show that we are able to perform time to depth corrections of the GPR data that provide a consistent image of the subsurface over the course of the experiment as moisture conditions change. for example, in experiments performed in a homogenous tank we show that the depth to the reflection produced at the bottom of the tank can be resolved with an error of ±5cm (±10%), where the error is mainly associated with picking errors. While this error can be somewhat reduced by improved traveltime picking, we have also found that hydrologic processes, e.g., the buildup of water at a seepage face formed at the bottom of the tank, contribute to complexity in the GPR data that requires analysis of wave interference for improved Interpretation. We suggest that multi-offset reflection GPR shows promise for both estimating velocity variations in the shallow subsurface and improving subsurface imaging.