ARTIFICIAL NEURAL-NETWORK-BASED SEISMIC DETECTOR

An artificial neural network-based pattern classification system is ap plied to seismic event detection. We have designed two types of Artifi cial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time ser ies as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and th en the trained AND's are applied to another set of recordings for test ing. Results show that the accuracy of the artificial neural network-b ased seismic detectors is better than that of the conventional algorit hms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.