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Cross-correlation attribute analysis of GPR data for tunnel engineering

Successful GPR cases related to defects detection to concrete liners of tunnels can be found in the last ten years. Generally, it was very difficult to get high quality data due to the limited data acquisition condition in tunnel, which might decrease the reliability of interpretation results. It is a great challenge to highlight the deep reflection events from the background with strong noise. Attribute analysis is an important tool that focused on the multi-properties of the signal. Here, cross-correlation attribute analysis has been proposed for GPR profile interpretation. It compares one trace with surrounding traces to determine degrees of similarity. improves the difference between the reflected wave from detection target and its surrounding mediums, which makes it easy to detect the anomaly that couldn't be found in original GPR time profile. A comparison between different kinds of time windows and sampling points is discussed to synthetic and real GPR data. Numerical simulation results shows that cross-correlation attribute analysis can effectively suppress background noises, low frequency disturbances and multiples. Application to the real data shows attribute analysis proved to be an effective method to detect the distribution of grouting layer behind tunnel lining.