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Discrimination of Earthquakes and Underwater Explosions Using Neural Networks

Osservatorio Vesuviano
INGV
Via Diocleziano,
Napoli, Italia
(E.D.P., F.G., M.M.)
Dip. Di Fisica "E.R. Caianiello"
Univ. di Salerno
INFM
Italia
(M.M., S.S.)
Instituto Italiano Alti Studi Scientifica
Vietri sul mare (SA)
Italia
(A.E., M.M.)
Wright State University
Dayton, Ohio 45435-0001
(A.E.)

Manuscript received 10 January 2002.

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.

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