Abstract
We report on the implementation of an automatic system able to discriminate
between explosion-generated artificial seismic events and local earthquakes
in the Phlegraean Fields (Italy). The explosions are fired weekly
at the sea bottom (tens of meters below sea level) by fishermen in
Pozzuoli bay; earthquakes are volcano-tectonic quakes with depths
shallower than 4 km. The discrimination system is based on an artificial
neural network and is composed of two modules. The first is devoted
to the extraction of the seismogram signatures and the second to
the classification of the seismic events into two classes. For the
features extraction (preprocessing stage), instead of the conventional
Fourier spectral analysis, we use a Linear Prediction Coding (LPC)
algorithm. This approach compresses the data from 256 samples to
only 7 parameters and can extract robust features for the spectrogram
representation. The classification is performed using a supervised
neural algorithm based on a Multilayer Neural Network (MLP) architecture.
We applied the method to a set of 30 seismic events recorded by the
stations of the local seismic network, 15 of which were generated
by the fishermen's explosions and 15 were volcano-tectonic earthquakes.
We dealt with a total of 280 records from different stations, 121
relating to explosions and 159 to earthquakes. Data were divided
in a training set containing 120 traces for earthquakes and 90 for
explosions, and a test set containing 70 traces corresponding to
39 records for earthquakes and 31 records for explosions. On the
test set the neural net gave a classification performance of 92\%,
indicating a good ability of the net to generalize. 10.1785/0120020005
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