Volcano-Seismic Event Detection and Clustering
This study looks into unsupervised and supervised methods for detecting events in volcano-seismic time series data, segmenting the data, and clustering the segments where there is activity. This two-stage pipeline allows for the analysis of the signals without requiring the type of event to be ident...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10978850/ |
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| _version_ | 1849723336872951808 |
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| author | Joe Carthy Pablo Rey-Devesa Manuel Titos Carmen Benitez |
| author_facet | Joe Carthy Pablo Rey-Devesa Manuel Titos Carmen Benitez |
| author_sort | Joe Carthy |
| collection | DOAJ |
| description | This study looks into unsupervised and supervised methods for detecting events in volcano-seismic time series data, segmenting the data, and clustering the segments where there is activity. This two-stage pipeline allows for the analysis of the signals without requiring the type of event to be identified at the offset and reduces the manpower required to analyze new data. Due to the resource intensive labeling process required to understand volcano-seismic signals it is important to explore unsupervised analysis techniques in this domain. The unsupervised methods are evaluated using supervised metrics including completeness, homogeneity, and V-measure scores. Alongside the unsupervised investigation, the use of intersection-based metrics that offer a clearer performance evaluation of the event segmentation task is motivated and the potential of gradient boosted trees for event detection is tested. |
| format | Article |
| id | doaj-art-d10dbdd323e8424782fce5929cfaabc0 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d10dbdd323e8424782fce5929cfaabc02025-08-20T03:11:03ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118112761128910.1109/JSTARS.2025.355941210978850Volcano-Seismic Event Detection and ClusteringJoe Carthy0https://orcid.org/0000-0003-1489-3279Pablo Rey-Devesa1https://orcid.org/0000-0002-6254-4930Manuel Titos2Carmen Benitez3https://orcid.org/0000-0002-5407-8335Department of Signal Theory, Telematics and Communications and the Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, SpainDepartment of Theoretical Physics and the Cosmos, University of Granada, Granada, SpainDepartment of Signal Theory, Telematics and Communications and the Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, SpainDepartment of Signal Theory, Telematics and Communications and the Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, SpainThis study looks into unsupervised and supervised methods for detecting events in volcano-seismic time series data, segmenting the data, and clustering the segments where there is activity. This two-stage pipeline allows for the analysis of the signals without requiring the type of event to be identified at the offset and reduces the manpower required to analyze new data. Due to the resource intensive labeling process required to understand volcano-seismic signals it is important to explore unsupervised analysis techniques in this domain. The unsupervised methods are evaluated using supervised metrics including completeness, homogeneity, and V-measure scores. Alongside the unsupervised investigation, the use of intersection-based metrics that offer a clearer performance evaluation of the event segmentation task is motivated and the potential of gradient boosted trees for event detection is tested.https://ieeexplore.ieee.org/document/10978850/Classificationclusteringdeep learningdimension reductiongradient boosted trees |
| spellingShingle | Joe Carthy Pablo Rey-Devesa Manuel Titos Carmen Benitez Volcano-Seismic Event Detection and Clustering IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification clustering deep learning dimension reduction gradient boosted trees |
| title | Volcano-Seismic Event Detection and Clustering |
| title_full | Volcano-Seismic Event Detection and Clustering |
| title_fullStr | Volcano-Seismic Event Detection and Clustering |
| title_full_unstemmed | Volcano-Seismic Event Detection and Clustering |
| title_short | Volcano-Seismic Event Detection and Clustering |
| title_sort | volcano seismic event detection and clustering |
| topic | Classification clustering deep learning dimension reduction gradient boosted trees |
| url | https://ieeexplore.ieee.org/document/10978850/ |
| work_keys_str_mv | AT joecarthy volcanoseismiceventdetectionandclustering AT pabloreydevesa volcanoseismiceventdetectionandclustering AT manueltitos volcanoseismiceventdetectionandclustering AT carmenbenitez volcanoseismiceventdetectionandclustering |