Abstract:
In support of the proposed Comprehensive Nuclear Test Ban Treaty, a large database of hydrophone recordings including T-phases, explosions, and noise has been compiled and cross referenced with known seismic events at the Center for Monitoring Research. Using this database, an automated hydroacoustic arrival detection and classification system has been developed. Detection is accomplished with a long-term-average/short-term-average power detector operating in several passbands. Station specific tuning of SNR thresholds and passband bounds allows the detector to trigger reliably on T-phases and explosions while passing over the majority of noise events such as whale calls. For each detected arrival, features such as duration, energy moments, spectral ratio, and order statistics are measured in multiple passbands from 2--85 Hz. A neural network uses these features to classify each arrival as signal or noise. Declared signals are passed to a second-stage network which classifies them as T-phases, explosions, or unknown events. T-phases arriving within a 4-min window around the time predicted from a seismic location are associated with that seismic event. These associations reveal relationships among event parameters such as location, magnitude, depth, duration, and coupling region.