An Improved Post-Processing Technique for Array-Based Detection of Underground Tunnels
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. To address this problem, this work proposes a data acquisition scheme and develops a new post-processing approach. The first applied measure estimates the confidence level of each detection result. The second processes the data in sub-arrays, and filters out false alarms. The third scans all detections and searches the cluster with the highest cumulative confidence level. This paper provides engineering practitioners with a simple and efficient method to reliably determine tunnel locations. Experimental results derived from data recorded in various testing sites and surface conditions verify the effectiveness of this work.
P. Xie et al., "An Improved Post-Processing Technique for Array-Based Detection of Underground Tunnels," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 3, pp. 828-837, Institute of Electrical and Electronics Engineers (IEEE), Mar 2014.
The definitive version is available at https://doi.org/10.1109/JSTARS.2013.2252000
Electrical and Computer Engineering
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Post-processing; Rayleigh wave; Sensor array; Tunnel detection; Confidence levels; Engineering practitioners; Natural conditions; Post-processing techniques; Surface conditions; Confidence levels; Engineering practitioners; Natural conditions; Underground tunnels
International Standard Serial Number (ISSN)
Article - Journal
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