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Thesis

Investigating large-scale change in volcanic time series data using machine learning analysis

Abstract:

Volcanic eruptions are characterised by large-scale transitions in behaviour, which include the transitions governing repose to unrest, unrest to eruption, and eruption to quiescence. Identification of these transitions is a fundamental goal in volcanology. This thesis aims to explore the use of machine learning approaches which are established for the monitoring of critical systems, such as healthcare or jet engine monitoring, to better understand the timing of large-scale transitions in vol...

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Division:
MPLS
Department:
Earth Sciences
Role:
Author

Contributors

Division:
MPLS
Department:
Earth Sciences
Sub department:
Earth Sciences
Role:
Supervisor
ORCID:
0000-0003-4259-7303
Role:
Supervisor
ORCID:
0000-0002-2663-9940
Role:
Supervisor
Role:
Supervisor
Role:
Examiner
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Name:
Natural Environment Research Council
Funder identifier:
http://dx.doi.org/10.13039/501100000270
Grant:
NE/L002612/1
Programme:
The Oxford DTP in Environmental Research
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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