Hc. Dai et C. Macbeth, AUTOMATIC PICKING OF SEISMIC ARRIVALS IN LOCAL EARTHQUAKE DATA USING AN ARTIFICIAL NEURAL-NETWORK, Geophysical journal international, 120(3), 1995, pp. 758-774
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).