A generalized deep learning model to detect and classify volcano seismicity
Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective...
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Volcanica
2025-06-01
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| Series: | Volcanica |
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| Online Access: | https://jvolcanica.org/ojs/index.php/volcanica/article/view/349 |
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| author | David Fee Darren Tan John Lyons Mariangela Sciotto Andrea Cannata Alicia Hotovec-Ellis Társilo Girona Aaron Wech Diana Roman Matthew Haney Silvio De Angelis |
| author_facet | David Fee Darren Tan John Lyons Mariangela Sciotto Andrea Cannata Alicia Hotovec-Ellis Társilo Girona Aaron Wech Diana Roman Matthew Haney Silvio De Angelis |
| author_sort | David Fee |
| collection | DOAJ |
| description | Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] (10.1029/2024JB029194) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network (VOISS-Net) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings. |
| format | Article |
| id | doaj-art-1bdabea9f17c4ec8b3ffb7070853019c |
| institution | Kabale University |
| issn | 2610-3540 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Volcanica |
| record_format | Article |
| series | Volcanica |
| spelling | doaj-art-1bdabea9f17c4ec8b3ffb7070853019c2025-08-20T03:44:51ZengVolcanicaVolcanica2610-35402025-06-018130532310.30909/vol/rjss1878334A generalized deep learning model to detect and classify volcano seismicityDavid Fee0https://orcid.org/0000-0002-0936-9977Darren Tan1https://orcid.org/0000-0001-8210-6041John Lyons2https://orcid.org/0000-0001-5409-1698Mariangela Sciotto3https://orcid.org/0000-0001-8711-1392Andrea Cannata4https://orcid.org/0000-0002-0028-5822Alicia Hotovec-Ellis5https://orcid.org/0000-0003-1917-0205Társilo Girona6https://orcid.org/0000-0001-6422-0422Aaron Wech7https://orcid.org/0000-0003-4983-1991Diana Roman8https://orcid.org/0000-0003-1282-5803Matthew Haney9Silvio De Angelis10https://orcid.org/0000-0003-2636-3056Alaska Volcano Observatory, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA.Alaska Volcano Observatory, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA.U.S. Geological Survey, Volcano Science Center, Alaska Volcano Observatory, Anchorage, AK, USA.Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo – Sezione di Catania, Catania, Italy.Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo – Sezione di Catania, Catania, Italy.U.S. Geological Survey, California Volcano Observatory, Moffett Field, CA, USA.Alaska Volcano Observatory, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA.U.S. Geological Survey, Volcano Science Center, Alaska Volcano Observatory, Anchorage, AK, USA.Earth and Planets Laboratory, Carnegie Institution for Science, Washington DC 20015, USA.U.S. Geological Survey, Volcano Science Center, Alaska Volcano Observatory, Anchorage, AK, USA.School of Environmental Sciences, University of Liverpool, Liverpool, England, UK.Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] (10.1029/2024JB029194) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network (VOISS-Net) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.https://jvolcanica.org/ojs/index.php/volcanica/article/view/349volcano seismicitymachine learningtremorexplosion |
| spellingShingle | David Fee Darren Tan John Lyons Mariangela Sciotto Andrea Cannata Alicia Hotovec-Ellis Társilo Girona Aaron Wech Diana Roman Matthew Haney Silvio De Angelis A generalized deep learning model to detect and classify volcano seismicity Volcanica volcano seismicity machine learning tremor explosion |
| title | A generalized deep learning model to detect and classify volcano seismicity |
| title_full | A generalized deep learning model to detect and classify volcano seismicity |
| title_fullStr | A generalized deep learning model to detect and classify volcano seismicity |
| title_full_unstemmed | A generalized deep learning model to detect and classify volcano seismicity |
| title_short | A generalized deep learning model to detect and classify volcano seismicity |
| title_sort | generalized deep learning model to detect and classify volcano seismicity |
| topic | volcano seismicity machine learning tremor explosion |
| url | https://jvolcanica.org/ojs/index.php/volcanica/article/view/349 |
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