Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design

Seismic swarms triggered by magma migration at volcanoes often imply an impending eruption. Using Seismic Amplitude Ratio Analysis (SARA) we have a tool to detect these migrations without having to do in-depth analysis of seismograms. This study examines the detection capability of SARA and introduc...

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Main Authors: James, K. E., Espinosa-Ortega, Tania, Tan, Chiou Ting, Taisne, Benoit
Other Authors: Asian School of the Environment
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172517
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1725172023-12-18T15:30:38Z Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design James, K. E. Espinosa-Ortega, Tania Tan, Chiou Ting Taisne, Benoit Asian School of the Environment Earth Observatory of Singapore Science::Geology Magma Migration Volcanic Hazard Seismic swarms triggered by magma migration at volcanoes often imply an impending eruption. Using Seismic Amplitude Ratio Analysis (SARA) we have a tool to detect these migrations without having to do in-depth analysis of seismograms. This study examines the detection capability of SARA and introduces a new method to quantify it for different seismic network designs for different magma migrations. This is computed using the location of the seismic stations as well as a detection threshold defined using real swarm data. As an example, we applied the analysis to the Piton de la Fournaise seismic network and computed the volume under the volcano where the seismic network is capable to detect vertical migrations. Moreover, we evaluated the impact removing or adding seismic stations has on the detection capability of the network. This method provides a quick and straightforward way for volcano observatories to identify any detection gaps in a network, as well as providing a way to identify the type of migration event a network is most likely to detect. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research was supported by the Earth Observatory of Singapore (EOS) via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. This work comprises EOS contribution number 479. We also thank the support of the National Research Foundation under ASE. EOS grant numbers 04MNS001816A620OST01, 04MNP001633C210I, NRF2018NRF-NSFC003ES-010. 2023-12-12T06:46:38Z 2023-12-12T06:46:38Z 2023 Journal Article James, K. E., Espinosa-Ortega, T., Tan, C. T. & Taisne, B. (2023). Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design. Journal of Volcanology and Geothermal Research, 435, 107769-. https://dx.doi.org/10.1016/j.jvolgeores.2023.107769 0377-0273 https://hdl.handle.net/10356/172517 10.1016/j.jvolgeores.2023.107769 2-s2.0-85148537859 435 107769 en 04MNS001816A620OST01 04MNP001633C210I NRF2018NRF-NSFC003ES-010 Journal of Volcanology and Geothermal Research © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Geology
Magma Migration
Volcanic Hazard
spellingShingle Science::Geology
Magma Migration
Volcanic Hazard
James, K. E.
Espinosa-Ortega, Tania
Tan, Chiou Ting
Taisne, Benoit
Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
description Seismic swarms triggered by magma migration at volcanoes often imply an impending eruption. Using Seismic Amplitude Ratio Analysis (SARA) we have a tool to detect these migrations without having to do in-depth analysis of seismograms. This study examines the detection capability of SARA and introduces a new method to quantify it for different seismic network designs for different magma migrations. This is computed using the location of the seismic stations as well as a detection threshold defined using real swarm data. As an example, we applied the analysis to the Piton de la Fournaise seismic network and computed the volume under the volcano where the seismic network is capable to detect vertical migrations. Moreover, we evaluated the impact removing or adding seismic stations has on the detection capability of the network. This method provides a quick and straightforward way for volcano observatories to identify any detection gaps in a network, as well as providing a way to identify the type of migration event a network is most likely to detect.
author2 Asian School of the Environment
author_facet Asian School of the Environment
James, K. E.
Espinosa-Ortega, Tania
Tan, Chiou Ting
Taisne, Benoit
format Article
author James, K. E.
Espinosa-Ortega, Tania
Tan, Chiou Ting
Taisne, Benoit
author_sort James, K. E.
title Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
title_short Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
title_full Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
title_fullStr Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
title_full_unstemmed Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
title_sort assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design
publishDate 2023
url https://hdl.handle.net/10356/172517
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