Abstract
Detecting, locating and characterizing volcanic eruptions at an early stage provides the best means to plan and mitigate against potential hazards. Here, we present an automatic system which is able to recognize and classify the main types of eruptive activity occurring at Mount Etna by exploiting infrared images acquired using thermal cameras installed around the volcano. The system employs a machine learning approach based on a Decision Tree tool and a Bag of Words-based classifier. The Decision Tree provides information on the visibility level of the monitored area, while the Bag of Words-based classifier detects the onset of eruptive activity and recognizes the eruption type as either explosion and/or lava flow or plume degassing/ash. Applied in real-time to each image of each of the thermal cameras placed around Etna, the proposed system provides two outputs, namely, visibility level and recognized eruptive activity status. By merging these outcomes, the monitored phenomena can be fully described from different perspectives to acquire more in-depth information in real time and in an automatic way.
Highlights
The training and testing phases of our system were performed using images acquired from all the thermThael ctraamineirnags aonpdertaetsitninggonphEatsneas douf roiunrgsvyosltceamniwc eevreenptesrafotrtmheedsuumsimngiticmraatgeerss aincq2u0i1r9e.dHfreorme, awlle tphreetsheenrtmthael coaumtceormasesopoebrtaatiinnegdofnorEatnnaerduuprtiinognvwohlciacnhicsteavrteendtsbaettwtheeensu1m8tmh iatncdra1te9rths ionf 2Ju01ly9,.fHoretrhe,e wvoelpcarnesicenatcttihveityouotfctohmee9sthobotfaSinepedtemfobr earnaenrdupfotirotnhewehxipchlossitvaertaedctibveittywoeef nth1e86ththaonfdD1e9ctehmobfeJru. ly, for the volcanic activity of the 9th of September and for the explosive activity of the 6th of December
On 15 July, 2019, the New Southeast Crater (NSEC) of Etna was active with sporadic explosions that Oconn1t5inJuuelyd, u20n1ti9l, 1th7eJuNley.wISnotuhteheeavsetnCinragteorf(1N8SJEuCly),oSftErotnmabwolaiasnacatcivtievwityithbescpaomraedmic oerxeplionsteionnsse, tchualtmcionnattiinnugewdituhntthile 1o7peJnuilnyg
We have introduced the first multiperspective Machine Learning (ML)-based system able to detect the onset and recognize the typology of eruptive activity occurring at the summit craters of the Etna volcano
Summary
The eruptions at summit craters can last from a few hours to several months, and evolve by phases of degassing alternating with Strombolian activity and, occasionally, paroxysms and lava fountains to lava overflows [5,6,7,8]. These different eruptive phases may generate a variety of hazards, including lava flows, gas emissions, explosions and tephra fall, which can represent a significant threat to people and property. The timely identification and reliable characterization of eruptive events is crucial to rapidly forecast the potential impact of hazardous phenomena and to support mitigation actions to reduce risk to people or critical infrastructure [9,10,11,12,13,14,15,16,17,18]
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