We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (VT). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer perceptron (MLP) with a single hidden layer. Spectral features of the signals and the parameterized attributes of their waveform have been used as input for this network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications

The volcano-tectonic seismicity occurring at Campi Flegrei caldera during its present unrest phase, started in 2005, is distributed into time-clustered events emerging from a background composed of earthquakes with higher inter-arrival times. Here, we show that clustered seismicity is cyclically recurrent at time scales from semidiurnal to annual, matching tidal and hydrological periodicities. These results suggest that volcano-tectonic seismicity at Campi Flegrei caldera is driven by both variations in the deep magmatic feeding system and exogenous phenomena, as rainfall or global inflation/deflation cycles of the Earth’s crust, controlled by the lunisolar interaction. Consequently, the role of exogenous triggers in the evolution of the present unrest phase should be properly considered in the elaboration of volcanic risk scenarios, presently limited to the study of surface indicators of deep phenomena.

New duration-based local (ML) and moment (Mw) magnitude scales are obtained for the Campi Flegrei area through analysis of a dataset of local volcano- tectonic earthquakes. First, the S-wave quality factor for the investigated area was experimentally calculated, and then the distance-correction curve, logA0� r� ,t o be used in the Richter formula MLlogAmax − logA0� r� , was numerically estimated by measuring the attenuation properties and, hence, propagating a synthetic S-wave packet in the earth medium. The local magnitude scale was normalized to fit the Richter formula that was valid for Southern California at a distance of 10 km. ML was estimated by synthesizing Wood-Anderson seismograms and measuring the maximum amplitude. For the same dataset, the moment magnitude was obtained from S-wave distance-corrected and site-corrected displacement spectra. Comparisons between local and moment magnitudes determined, along with the old duration magnitude (MD) routinely used at the Istituto Nazionale di Geofisica e Vulcanologia- Osservatorio Vesuviano, are presented and discussed. Moreover, the relationships between ML and Mw calculated for two reference sites are also derived.