Deep learning and multi-station classification of volcano-seismic events of the Nevados del Chillán volcanic complex (Chile)

Alejandro Ferreira, Millaray Curilem, Walter Gómez, Ricardo Araújo Rios

Abstract: This paper presents a methodology for developing a volcano-seismic event classification system using a multi-station deep learning approach to support monitoring the Nevados del Chillán Volcanic Complex, which has been active since 2017. A convolutional network of multiple inputs processes the information from an event recorded up to five seismic stations. Each record is represented by its normalized spectrogram; thus, the network may receive from one to five spectrograms as input. The design includes entering additional information into the network, like the stations configuration and the event duration, information not provided by the spectrograms. Finally, this work includes the design and implementation of a relational database to access the continuous traces of events, showing different subsets of data quickly and efficiently. The results show that the classification of an event recorded up to five stations is substantially more effective than a single-station strategy. However, incorporating additional information of the signal does not significantly improve the classification performance.