Pattern recognition applied to volcanic activity: Identification of the precursory patterns to Etna recent flank eruptions and periods of rest

Computational pattern recognition is an invaluable tool in understanding the phenomenology of complex processes and represents the first step towards their effective physical modeling. So far it has never been used in volcanology. We discuss in detail pattern recognition algorithms of the "logic" type and present an application to the recent eruptive activity of Mount Etna volcano. The specific aim is a characterization of the intermediate-term precursory patterns to its flank eruptions. A comparatively successful recognition is obtained, providing the combinations of parameters which have been precursory to eruptions and periods of rest in the last fifteen years. The recognized patterns yield two main results: (a) the seismicity in the Gulf of Patti is identified as the most important precursor, and a further correlation study confirms this issue as highly significant, implying that regional tectonic stress, and in particular the structures around the Tindari-Giardini lineament, play a fundamental role in triggering Etna flank activity; (b) an operative prediction-oriented application of the recognized precursory patterns is tentatively possible. © 1991.