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A deep active learning approach to the automatic classification of volcano-seismic events

Abstract:

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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Publisher copy:
10.3389/feart.2022.807926

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Earth Sciences
Oxford college:
University College
Role:
Author
ORCID:
0000-0003-4259-7303
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:
2296-6463
Language:
English
Keywords:
Pubs id:
1232452
Local pid:
pubs:1232452
Deposit date:
2022-01-14

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