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Authors: Esposito, A. M.* 
D'Auria, L.* 
Angelillo, A.* 
Giudicepietro, F.* 
Martini, M.* 
Title: Predictive analysis of the seismicity level at Campi Flegrei volcano using a data-driven approach
Issue Date: 2014
Publisher: Springer International Publishing Switzerland 2014
ISBN: 978-3-319-04128-5
Keywords: Campi Flegrei volcano
seismicity forecast
MLP neural networks
Subject Classification04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology 
Abstract: This work aims to provide a short-term tool to estimate the possible trend of the seismicity level in the area of Campi Flegrei (southern Italy) for Civil Protection purposes. During the last relevant period of seismic activity, between 1982 and 1984, an uplift of the ground (bradyseism) of more than 1.5 m occurred. It was accompanied by more than 16,000 earthquakes up to magnitude 4.2 which forced the civil authorities to order the evacuation of about 40,000 people from Pozzuoli town for several months. Scientific studies evidenced a temporal correlation between these geophysical phenomena. This has led us to consider a data-driven approach to obtain a forecast of the seismicity level for this area. In particular, a technique based on a Multilayer Perceptron (MLP) network has been used for this intent. Neural networks are data processing mechanisms capable of relating input data with output ones without any prior correlation model but only using empirical evidences obtained from the analysis of available data. The proposed method has been tested on a set of seismic and deformation data acquired between 1983 and 1985 and then including the data of the aforementioned crisis which affected the Campi Flegrei. Once defined the seismicity levels on the basis of the maximum magnitude recorded within a week, three MLP networks were implemented with respectively 2, 3 and 4 output classes. The first network (2 classes) provides only an indication about the possible occurrence of earthquakes felt by people (with magnitude higher than 1.7), while the remaining nets (3 and 4 classes) give also a rough suggestion of their intensity. Furthermore, for these last two networks one of the output classes allows to obtain a forecast about the possible occurrence of strong potentially damaging earthquakes with magnitude higher than 3.5. Each network has been trained on a fixed interval and then tested for the forecast on the subsequent period. The results show that the performance decreases as a function of the complexity of the examined task that is the number of covered classes. However, the obtained results are very promising, for which the proposed system deserves further studies since it could be of support to the Civil Protection operations in the case of possible future crises.
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