Automatic Detection of Long Period Events Based on Subband-Envelope Processing

Abstract

This work presents a novel approach to automatic detection of long period events (LP) in continuous seismic records. Without any supervised learning, the proposal is based on a simple processing to search for the LP characteristic shape, duration, and band of activity. Continuous raw signals from the seismometer are first filtered into three frequency bands separating lower, central, and upper frequency components. These new signals are then processed in parallel to extract subband envelopes and create a characteristic function that enhances LP features. Experiments to test the proposal are presented using: 1) 2 h of continuous recordings of the Volcano of Deception Island, Antarctica, containing LP events artificially contaminated with seismic background noise to create low signal-to-noise ratio scenarios and 2) a set of earthquake-like computer generated signals, randomly produced and inserted in the continuous records to recreate a testing environment as challenging as possible. A receiver operating curve analysis of the results compared to those of a classical short/long time average approach, provides positive conclusions on the performance of the technique presented.