Buried Object Detection based on Acousto-seismic Method using Accelerometer and Neural Network

Abstract

: A system for detecting buried objects is often needed for inspection, exploration and security purposes. This research has developed a system to detect buried objects based on the acousto-seismic principle. A sinusoidal signal is amplified by an audio amplifier to drive a subwoofer speaker to produce mechanical vibrations. The seismic vibrations propagating in the ground are measured by an accelerometer. The Fast Fourier Transform method converts vibrations in the time domain to the frequency domain. Neural Network algorithm is applied to distinguish these wave spectrums to determine buried objects. After testing in experiments, this system can distinguish between buried metal and non-metal objects. This system could also recognize the shallow buried objects with an accuracy rate of 86.6%. This method can be potentially developed to detect land mines both metal and non-metal materials.