Feasibility study of the automated detection and localization of underground tunnel excavation using Brillouin optical time domain reflectometer

Abstract

Cross-borders smuggling tunnels enable unmonitored movement of people, drugs and weapons and pose a very serious threat to homeland security. Recent advances in strain measurements using optical fibers allow the development of smart underground security fences that could detect the excavation of smuggling tunnels. This paper presents the first stages in the development of such a fence using Brillouin Optical Time Domain Reflectometry (BOTDR). In the simulation study, two different ground displacement models are used in order to evaluate the robustness of the system against imperfect modeling. In both cases, soil-fiber interaction is considered. Measurement errors, and surface disturbances (obtained from a field test) are also included in the calibration and validation stages of the system. The proposed detection system is based on wavelet decomposition of the BOTDR signal, followed by a neural network that is trained to recognize the tunnel signature in the wavelet coefficients.The results indicate that the proposed system is capable of detecting even small tunnel (0.5m diameter) as deep as 20 meter.

Recent development in artificial neural network based distributed fiber optic sensors

“…In 2009, A. Klar et al [20] demonstrated a 76 m long underground tunnel monitoring using BOTDR system followed by a neural network that is trained to recognize the tunnel status. The ability of the sensing system to detect tunnel activities with pattern recognition of external disturbances, while insensitive to measurement noise and ground disturbances.…”

Optical Fiber Sensors in Physical Intrusion Detection Systems: A Review

“…Klar and Linker [93] reported a system specifically designed for detecting underground tunneling used for smuggling, using smart underground security fences. The detection system was based on wavelet decomposition of Brillouin optical time domain reflectometry (BOTDR) and was capable of detecting tunneling vibrations whilst also being insensitive to above ground noise.…”

RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

“…However, during the processing, plenty of wavelet coefficients are generated by the wavelet packet decomposition, which can induce the complexity of training network, since it is directly proportional to the number of training samples. It is known that the multi-level wavelet decomposition has the nature with fewer wavelet coefficients than the wavelet packet decomposition [13,14]. Therefore, it is more suitable for the neural networks classification.…”