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Recognizing the mechanisms underlying seismic activity and tracking temporal and spatial patterns of earthquakes represent primary inputs to monitor active volcanoes and forecast eruptions. To quantify this seismicity, catalogs are established to summarize the history of the observed types and number of volcano-seismic events. In volcano observatories the detection and posterior classification or labeling of the events is manually performed by technicians, often suffering a lack of unified criteria and eventually resulting in poorly reliable labeled databases. State-of-the-art automatic Volcano-Seismic Recognition (VSR) systems allow real-time monitoring and consistent catalogs. VSR systems are generally designed to monitor one station of one volcano, decreasing their efficiency when used to recognize events from another station, in a different eruptive scenario or at different volcanoes. We propose a Volcano-Independent VSR (VI.VSR) solution for creating an exportable VSR system, whose aim is to generate labeled catalogs for observatories which do not have the resources for deploying their own systems. VI.VSR trains universal recognition models with data of several volcanoes to obtain portable and robust characteristics. We have designed the VULCAN.ears ecosystem to facilitate the VI.VSR application in observatories, including the pyVERSO tool to perform VSR tasks in an intuitive way, its graphical interface, geoStudio, and liveVSR for real-time monitoring. Case studies are presented at Deception, Colima, Popocatépetl and Arenal volcanoes testing VI.VSR models in challenging scenarios, obtaining encouraging recognition results in the 70–80% accuracy range. VI.VSR technology represents a major breakthrough to monitor volcanoes with minimal effort, providing reliable seismic catalogs to characterise real-time changes.

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This paper evaluates different Restricted Boltzmann Machines models in unsupervised, semi-supervised and supervised frameworks using information from human actions. After feeding these multilayer models with low level features, we infer high-level discriminating features that highly improve the classification performance. This approach eliminates the difficult process of selecting good mid-level feature descriptors, changing the feature selection and extraction process by a learning stage. Two main contributions are presented. First, a new sequence-descriptor from accumulated histograms of optical flow (aHOF) is presented. Second, comparative results using unsupervised, supervised and semisupervised classification experiments are shown. The results show that the RBM architectures provide very good results in our classification task and present very good properties for semi-supervised learning.

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