Semi-Automated Inversion-Specific Data Selection for Volcano Tomography

Autores
Guardo, Roberto Antonino; De Siena, Luca
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Guardo, Roberto Antonino. Universidad Nacional de Río Negro. Instituto de Investigación en Paleobiología y Geología. Río Negro; Argentina.
Fil: De Siena, Luca. Johannes Gutenberg University. Mainz; Germany
Active seismic experiments allow reconstructing the subsurface structure of volcanoes with unprecedented resolution and are vital to improve the interpretation of volcanic processes. They require a quality assessment for thousands of seismic waveforms recorded at hundreds of stations in the shortest amount of time. However, the processing necessary to obtain reliable images from such massive datasets demands signal processing and selection strategies specific to the inversions attempted. Here, we present a semi-automated workflow for data selection and inversion of amplitude-dependent information using the original TOMODEC2005 dataset, recorded at Deception Island (Antarctica). The workflow is built to tomographic techniques using amplitude information, and can be generalised to passive seismic imaging. It first selects data depending on standard attributes, like the presence of zeroes across all seismic waveforms. Then, waveform selections depend on inversion-specific attributes, like the delay of the maximum amplitude of the waveform or the quality of coda-wave decays. The automatic workflow and final visual selections produce a dataset reconstructing anomalies at a node spacing of 2 km, imaging a high-attenuation anomaly in the centre of the Deception Island bay, consistent with previously-published maps. Attenuation models are then obtained at a node spacing of 1 km, highlighting bodies of highest attenuation scattered across the island and a NW-SE trend in the high-attenuation anomaly in the central bay. These results show the effect of the local extension regime on volcanic structures, providing details on the eruptive history and evolution of the shallow magmatic and hydrothermal systems. The selection workflow can be easily generalised to other amplitude-dependent tomographic techniques when applied to active seismic surveys. Image improvements from the original dataset are minor when selecting data using standard attributes, like signal-to-noise ratios. Tomographic maps become drastically more stable and consistent between different frequencies and resolutions when data selection targets attributes specific to the inversion.
-
Materia
Ciencias Exactas y Naturales
Seismic Tomography
Data Processing
Big Data
Volcano Imaging
Active Seismicity
Data Cleaning
Ciencias Exactas y Naturales
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
RID-UNRN (UNRN)
Institución
Universidad Nacional de Río Negro
OAI Identificador
oai:rid.unrn.edu.ar:20.500.12049/9346

id RIDUNRN_a086ea862a486a6d3cf132520428a617
oai_identifier_str oai:rid.unrn.edu.ar:20.500.12049/9346
network_acronym_str RIDUNRN
repository_id_str 4369
network_name_str RID-UNRN (UNRN)
spelling Semi-Automated Inversion-Specific Data Selection for Volcano TomographyGuardo, Roberto AntoninoDe Siena, LucaCiencias Exactas y NaturalesSeismic TomographyData ProcessingBig DataVolcano ImagingActive SeismicityData CleaningCiencias Exactas y NaturalesFil: Guardo, Roberto Antonino. Universidad Nacional de Río Negro. Instituto de Investigación en Paleobiología y Geología. Río Negro; Argentina.Fil: De Siena, Luca. Johannes Gutenberg University. Mainz; GermanyActive seismic experiments allow reconstructing the subsurface structure of volcanoes with unprecedented resolution and are vital to improve the interpretation of volcanic processes. They require a quality assessment for thousands of seismic waveforms recorded at hundreds of stations in the shortest amount of time. However, the processing necessary to obtain reliable images from such massive datasets demands signal processing and selection strategies specific to the inversions attempted. Here, we present a semi-automated workflow for data selection and inversion of amplitude-dependent information using the original TOMODEC2005 dataset, recorded at Deception Island (Antarctica). The workflow is built to tomographic techniques using amplitude information, and can be generalised to passive seismic imaging. It first selects data depending on standard attributes, like the presence of zeroes across all seismic waveforms. Then, waveform selections depend on inversion-specific attributes, like the delay of the maximum amplitude of the waveform or the quality of coda-wave decays. The automatic workflow and final visual selections produce a dataset reconstructing anomalies at a node spacing of 2 km, imaging a high-attenuation anomaly in the centre of the Deception Island bay, consistent with previously-published maps. Attenuation models are then obtained at a node spacing of 1 km, highlighting bodies of highest attenuation scattered across the island and a NW-SE trend in the high-attenuation anomaly in the central bay. These results show the effect of the local extension regime on volcanic structures, providing details on the eruptive history and evolution of the shallow magmatic and hydrothermal systems. The selection workflow can be easily generalised to other amplitude-dependent tomographic techniques when applied to active seismic surveys. Image improvements from the original dataset are minor when selecting data using standard attributes, like signal-to-noise ratios. Tomographic maps become drastically more stable and consistent between different frequencies and resolutions when data selection targets attributes specific to the inversion.-Frontiers2022-04-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfGuardo, R and De Siena, L (2022) Semi-Automated Inversion-Specific Data Selection for Volcano Tomography. Front. Earth Sci. 10; 849152.2296-6463https://www.frontiersin.org/articles/10.3389/feart.2022.849152/fullhttp://rid.unrn.edu.ar/handle/20.500.12049/9346https://doi.org/10.3389/feart.2022.849152enghttps://www.frontiersin.org/journals/earth-science10Frontiers in Earth Scienceinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/reponame:RID-UNRN (UNRN)instname:Universidad Nacional de Río Negro2025-09-29T14:28:50Zoai:rid.unrn.edu.ar:20.500.12049/9346instacron:UNRNInstitucionalhttps://rid.unrn.edu.ar/jspui/Universidad públicaNo correspondehttps://rid.unrn.edu.ar/oai/snrdrid@unrn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:43692025-09-29 14:28:50.608RID-UNRN (UNRN) - Universidad Nacional de Río Negrofalse
dc.title.none.fl_str_mv Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
title Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
spellingShingle Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
Guardo, Roberto Antonino
Ciencias Exactas y Naturales
Seismic Tomography
Data Processing
Big Data
Volcano Imaging
Active Seismicity
Data Cleaning
Ciencias Exactas y Naturales
title_short Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
title_full Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
title_fullStr Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
title_full_unstemmed Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
title_sort Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
dc.creator.none.fl_str_mv Guardo, Roberto Antonino
De Siena, Luca
author Guardo, Roberto Antonino
author_facet Guardo, Roberto Antonino
De Siena, Luca
author_role author
author2 De Siena, Luca
author2_role author
dc.subject.none.fl_str_mv Ciencias Exactas y Naturales
Seismic Tomography
Data Processing
Big Data
Volcano Imaging
Active Seismicity
Data Cleaning
Ciencias Exactas y Naturales
topic Ciencias Exactas y Naturales
Seismic Tomography
Data Processing
Big Data
Volcano Imaging
Active Seismicity
Data Cleaning
Ciencias Exactas y Naturales
dc.description.none.fl_txt_mv Fil: Guardo, Roberto Antonino. Universidad Nacional de Río Negro. Instituto de Investigación en Paleobiología y Geología. Río Negro; Argentina.
Fil: De Siena, Luca. Johannes Gutenberg University. Mainz; Germany
Active seismic experiments allow reconstructing the subsurface structure of volcanoes with unprecedented resolution and are vital to improve the interpretation of volcanic processes. They require a quality assessment for thousands of seismic waveforms recorded at hundreds of stations in the shortest amount of time. However, the processing necessary to obtain reliable images from such massive datasets demands signal processing and selection strategies specific to the inversions attempted. Here, we present a semi-automated workflow for data selection and inversion of amplitude-dependent information using the original TOMODEC2005 dataset, recorded at Deception Island (Antarctica). The workflow is built to tomographic techniques using amplitude information, and can be generalised to passive seismic imaging. It first selects data depending on standard attributes, like the presence of zeroes across all seismic waveforms. Then, waveform selections depend on inversion-specific attributes, like the delay of the maximum amplitude of the waveform or the quality of coda-wave decays. The automatic workflow and final visual selections produce a dataset reconstructing anomalies at a node spacing of 2 km, imaging a high-attenuation anomaly in the centre of the Deception Island bay, consistent with previously-published maps. Attenuation models are then obtained at a node spacing of 1 km, highlighting bodies of highest attenuation scattered across the island and a NW-SE trend in the high-attenuation anomaly in the central bay. These results show the effect of the local extension regime on volcanic structures, providing details on the eruptive history and evolution of the shallow magmatic and hydrothermal systems. The selection workflow can be easily generalised to other amplitude-dependent tomographic techniques when applied to active seismic surveys. Image improvements from the original dataset are minor when selecting data using standard attributes, like signal-to-noise ratios. Tomographic maps become drastically more stable and consistent between different frequencies and resolutions when data selection targets attributes specific to the inversion.
-
description Fil: Guardo, Roberto Antonino. Universidad Nacional de Río Negro. Instituto de Investigación en Paleobiología y Geología. Río Negro; Argentina.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-25
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Guardo, R and De Siena, L (2022) Semi-Automated Inversion-Specific Data Selection for Volcano Tomography. Front. Earth Sci. 10; 849152.
2296-6463
https://www.frontiersin.org/articles/10.3389/feart.2022.849152/full
http://rid.unrn.edu.ar/handle/20.500.12049/9346
https://doi.org/10.3389/feart.2022.849152
identifier_str_mv Guardo, R and De Siena, L (2022) Semi-Automated Inversion-Specific Data Selection for Volcano Tomography. Front. Earth Sci. 10; 849152.
2296-6463
url https://www.frontiersin.org/articles/10.3389/feart.2022.849152/full
http://rid.unrn.edu.ar/handle/20.500.12049/9346
https://doi.org/10.3389/feart.2022.849152
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.frontiersin.org/journals/earth-science
10
Frontiers in Earth Science
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:RID-UNRN (UNRN)
instname:Universidad Nacional de Río Negro
reponame_str RID-UNRN (UNRN)
collection RID-UNRN (UNRN)
instname_str Universidad Nacional de Río Negro
repository.name.fl_str_mv RID-UNRN (UNRN) - Universidad Nacional de Río Negro
repository.mail.fl_str_mv rid@unrn.edu.ar
_version_ 1844621600672972800
score 12.559606