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
- Institución
- Universidad Nacional de Río Negro
- OAI Identificador
- oai:rid.unrn.edu.ar:20.500.12049/9346
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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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/4.0/ |
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openAccess |
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