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A connectionist approach for automatic labeling of regional seismic phases using a single vertical component seismogram
Author(s) -
Mousset E.,
Cansi Y.,
Crusem R.,
Souchet Y.
Publication year - 1996
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/95gl03811
Subject(s) - seismogram , artificial neural network , spectrogram , component (thermodynamics) , connectionism , geology , identification (biology) , range (aeronautics) , computer science , seismology , artificial intelligence , pattern recognition (psychology) , botany , materials science , biology , composite material , physics , thermodynamics
We present an automatic system for regional seismic phase identification from monocomponent single station records. It is based on a neural network study of the spectrogram. A large dataset of regional events checked by experts has been used for the training step. A sophisticated neural network design allows the system to take into account the variability of the different regional seismic phases in a wide magnitude and distance range. On the training and test sets respectively, more than 85% and 70% of the data are correctly classified.