An Application Example of Machine Learning to Shallow Subsurface Exploration using Ground Penetrating Radar

Abstract

We applied a supervised machine learning method for the purpose of auto detecting cavities under paved roadway from GPR （Ground Penetrating Radar） images. In cavity detection surveys under paved road way, we typically use vehicle-born GPR systems for the necessity to cover wide survey area without any traffic controls. To detect cavity from massive data acquired by vehicle-borne GPR swiftly, skilled-engineers carefully interpret GPR data considering features based on physics of GPR response. Automatic process based on knowledges of skilled-engineers is required, although automatic anomaly detection has not practically realized cause of un-uniform or complex responses from cavities. We applied machine learning methods to detect cavity anomalies using actual GPR data which were acquired on natural occurred cavities as training data. At the veri?cation with actual survey data, our method was able to detect cavitycaused GPR patterns. This will be helpful for analyzer to narrow down cavity responses, meanwhile there were still too many over detections. We think accumulation of labeled GPR data of cavity will also contribute to improvement of our method.