Identifying earthquake swarms at Mt. Ruapehu, New Zealand: a machine learning approach

Mitchenson, Sam, Johnson, Jessica H. ORCID: https://orcid.org/0000-0003-3628-6402, Milner, Ben and Lines, Jason ORCID: https://orcid.org/0000-0002-1496-5941 (2024) Identifying earthquake swarms at Mt. Ruapehu, New Zealand: a machine learning approach. Frontiers in Earth Science, 12. ISSN 2296-6463

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Abstract

Mt. Ruapehu is an active andesitic stratovolcano, consisting of several peaks with the summit plateau at 2,797 m, making it the tallest active volcano in New Zealand. The extent of the volcano spreads 40 km across with a series of complex faults encompassing almost the entire base of the volcano. A series of earthquakes occurring 20 km west of the summit of Mt. Ruapehu, near the small town of Erua, which preceded the 1995/1996 major volcanic eruption sequence has been proposed as a medium-term precursor for eruptions at Mt. Ruapehu. We use unsupervised machine learning clustering algorithms HDBSCAN and DBSCAN to define anomalous earthquake swarms in the region and determine whether the Erua swarm was unique by identifying key characteristics in space, time and magnitude distribution. HDBSCAN found six spatial cluster zones to the west of Mt. Ruapehu, which have temporal seismic bursts of activity between 1994 and 2023. DBSCAN identified the seismic swarm that preceded the 1995/1996 major eruption, along with one other similar cluster in the same region, which did not coincide with any documented magmatic unrest, suggesting distal seismic swarms at Mt. Ruapehu may not serve as a reliable eruption precursor when observed in isolation. We instead found that earthquake swarms are relatively common at Mt. Ruapehu and the temporal evolution of the earthquake clusters west of Mt. Ruapehu share similar characteristics to seismic swarms identified in other settings related to fluid migration, typical of fault-valve models.

Item Type:	Article
Additional Information:	Data availability statement: Publicly available datasets were analyzed in this study. This data can be found here: https://quakesearch.geonet.org.nz/. Funding information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors would like to express their gratitude to ARIES and NERC for the financial support provided for this research; grant number NE/S007334/1.
Faculty \ School:	Faculty of Science > School of Environmental Sciences Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Data Science and AI
Depositing User:	LivePure Connector
Date Deposited:	15 Jul 2024 08:31
Last Modified:	10 Dec 2024 01:44
URI:	https://ueaeprints.uea.ac.uk/id/eprint/95915
DOI:	10.3389/feart.2024.1343874

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