AUTOMATIC PICKING OF SEISMIC ARRIVALS IN LOCAL EARTHQUAKE DATA USING AN ARTIFICIAL NEURAL-NETWORK

A preliminary study is performed to test the ability of an artificial neural network (ANN) to detect and pick seismic arrivals from local ea rthquake data, This is achieved using three-component recordings by ut ilizing the vector modulus of these seismic records as the network inp ut. A discriminant function, F(t), determined from the output of the t rained ANN, is then employed to define the arrival onset. 877 pre-trig gered recordings from two stations in a local earthquake network are a nalysed by an ANN trained with only nine P waves and nine noise segmen ts. The data have a range of magnitudes (M(L)) from -0.3 to 1.0, and s ignal-to-noise ratios from 1 to 200. Comparing the results with manual picks, the ANN can accurately detect 93.9 per cent of the P waves and also 90.3 per cent of the S waves with a F(t) threshold set at 0.6 (m aximum is 1.0), These statistics do not include false alarms due to ot her non-seismic signals or unusable records due to excessive noise, In 17.2 per cent of the cases the ANN detected false alarms prior to the event. Determining the onset times by using the local maximum of F(t) , we find that 75.4 per cent of the P-wave estimates and 66.7 per cent of the S-wave estimates are within one sample increment (10 ms) of th e reference data picked manually. Only 7.7 per cent of the P-wave esti mates and 11.8 per cent of the S-wave estimates are inaccurate by more than five sample increments (50 ms). The majority of these records ha ve distinct local P and S waves. The ANN also works for seismograms wi th low signal-to-noise ratios, where visual examination is difficult, The examples show the adaptive nature of the ANN, and that its ability to pick may be improved by adding or adjusting the training data. The ANN has potential as a tool to pick arrivals automatically. This algo rithm has been adopted as a component in the early stages of our devel opment of an automated subsystem to analyse local earthquake data, Fur ther potential applications for the neural network include editing of poor traces (before present algorithm) and rejection of false alarms ( after this present algorithm).