Neural analysis of seismic data: applications to the monitoring of Mt. Vesuvius

The computing techniques currently available for the seismic monitoring allow advanced analysis. However, the correct event classification remains a critical aspect for the reliability of real time automatic analysis. Among the existing methods, neural networks may be considered efficient tools for detection and discrimination, and may be integrated into intelligent systems for the automatic classification of seismic events. In this work we apply an unsupervised technique for analysis and classification of seismic signals recorded in the Mt. Vesuvius area in order to improve the automatic event detection. The examined dataset contains about 1500 records divided into four typologies of events: earthquakes, landslides, artificial explosions, and "other" (any other signals not included in the previous classes). First, the Linear Predictive Coding (LPC) and a waveform parametrization have been applied to achieve a significant and compact data encoding. Then, the clustering is obtained using a Self-Organizing Map (SOM) neural network which does not require an a-priori classification of the seismic signals, groups those with similar structures, providing a simple framework for understanding the relationships between them. The resulting SOM map is separated into different areas, each one containing the events of a defined type. This means that the SOM discriminates well the four classes of seismic signals. Moreover, the system will classify a new input pattern depending on its position on the SOM map. The proposed approach can be an efficient instrument for the real time automatic analysis of seismic data, especially in the case of possible volcanic unrest.