A generalized deep learning model to detect and classify volcano seismicity
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| Title: | A generalized deep learning model to detect and classify volcano seismicity |
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| Authors: | David Fee, Darren Tan, John Lyons, Mariangela Sciotto, Andrea Cannata, Alicia Hotovec-Ellis, Társilo Girona, Aaron Wech, Diana Roman, Matthew Haney, Silvio De Angelis |
| Source: | Volcanica, Vol 8, Iss 1, Pp 305-323 (2025) |
| Publisher Information: | Volcanica, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Machine Learning, QE1-996.5, Volcano Seismicity, machine learning, Explosion, Tremor, volcano seismicity, Geology, explosion, tremor |
| 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. |
| Document Type: | Article |
| File Description: | application/pdf |
| ISSN: | 2610-3540 |
| DOI: | 10.30909/vol/rjss1878 |
| Access URL: | https://doaj.org/article/1bdabea9f17c4ec8b3ffb7070853019c https://hdl.handle.net/20.500.11769/678729 https://doi.org/10.30909/vol/rjss1878 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....dbdf90daefa437862d2b229ae6c76a35 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | 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. |
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| ISSN: | 26103540 |
| DOI: | 10.30909/vol/rjss1878 |