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The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings

Dai, Hengchang; MacBeth, Colin. 1997 The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings. Journal of Geophysical Research, 102 (B7). 15105-15113. 10.1029/97JB00625

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Abstract/Summary

An automatic approach is developed to pick P and S arrivals from single component (1-C) recordings of local earthquake data. In this approach a back propagation neural network (BPNN) accepts a normalized segment (window of 40 samples) of absolute amplitudes from the 1-C recordings as its input pattern, calculating two output values between 0 and 1. The outputs (0,1) or (1,0) correspond to the presence of an arrival or background noise within a moving window. The two outputs form a time series. The P and S arrivals are then retrieved from this series by using a threshold and a local maximum rule. The BPNN is trained by only 10 pairs of P arrivals and background noise segments from the vertical component (V-C) recordings. It can also successfully pick seismic arrivals from the horizontal components (E-W and N-S). Its performance is different for each of the three components due to strong effects of ray path and source position on the seismic waveforms. For the data from two stations of TDP3 seismic network, the success rates are 93%, 89%, and 83% for P arrivals and 75%, 91%, and 87% for S arrivals from the V-C, E-W, and N-S recordings, respectively. The accuracy of the onset times picked from each individual 1-C recording is similar. Adding a constraint on the error to be 10 ms (one sample increment), 66%, 59% and 63% of the P arrivals and 53%, 61%, and 58% of the S arrivals are picked from the V-C, E-W and N-S recordings respectively. Its performance is lower than a similar three-component picking approach but higher than other 1-C picking methods.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1029/97JB00625
Programmes: BGS Programmes
ISSN: 0148-0227
Date made live: 15 Oct 2012 15:57
URI: http://nora.nerc.ac.uk/id/eprint/19928

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