Journal article
A deep active learning approach to the automatic classification of volcano-seismic events
- Abstract:
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Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model ac...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 1.9MB)
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- Publisher copy:
- 10.3389/feart.2022.807926
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Authors
Bibliographic Details
- Publisher:
- Frontiers Media Publisher's website
- Journal:
- Frontiers in Earth Science Journal website
- Volume:
- 10
- Article number:
- 807926
- Publication date:
- 2022-02-15
- Acceptance date:
- 2022-01-13
- DOI:
- EISSN:
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2296-6463
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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1232452
- Local pid:
- pubs:1232452
- Deposit date:
- 2022-01-14
Terms of use
- Copyright holder:
- Manley et al.
- Copyright date:
- 2022
- Rights statement:
- ©2022 Manley, Mather, Pyle, Clifton, Rodgers, Thompson and Londono. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Licence:
- CC Attribution (CC BY)
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