Array processing for underground tunnel detection

Abstract

This paper investigates challenges faced by many geophysical algorithms applied to real-world cases such as the Attenuation Analysis of Rayleigh Waves (AARW). AARW shows great promise in terms of detecting shallow underground tunnels. However, in-situ subsurface anomalies, including those due to anisotropy, and instrument sensitivity to natural conditions can significantly degrade the utility of this technique. The first applied measure estimates the confidence level of each detection result. The second processes the recorded data in sub-arrays, acting as a filter to remove false alarms. The third scans all detections and searches the cluster with the highest cumulative confidence level. A case study is presented to demonstrate the effectiveness of AARW along with post-processing quality control measures. This work provides engineering practitioners with a simple and efficient method to reliably determine tunnel locations.