Performance Evaluation of a Real-Time Seismic Detection System Based on CFAR Detectors

Abstract

In this research work, we present a novel application specific Ordered cell averaging-CFAR (OCA-CFAR) detector for anthropogenic seismic events. We investigate the use of geophones and real-time hardware for the detection of moving targets such as vehicle, human footsteps and activities such as hammering on the ground. We present here energy assisted variants of Constant False Alarm Rate (CFAR) detector implemented on a real-time embedded platform to obtain detection cues of the ground seismic source. The OCA-CFAR algorithm has been modified in terms of window length and position of Cell Under Test (CUT), suitably adapted to the nature of seismic events engendered from moving vehicles and humans. The proposed detector has been compared with the popularly known variants such as Cell Averaging-CFAR, Least Of-CFAR, Greatest Of-CFAR and Ordered Statistics-CFAR for probabilities of false alarm ranging from 1e-1 to 1e-5. The proposed method shows an increased detection rate with minimized false alarms on the field trials and a dataset containing the seismic signature of human activities and some of civilian vehicles.