Semi-Automated Inversion-Specific Data Selection for Volcano Tomography
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 wit...
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ftunivnrionegro:oai:rid.unrn.edu.ar:20.500.12049/9346 2023-05-15T13:37:25+02:00 Semi-Automated Inversion-Specific Data Selection for Volcano Tomography Guardo, Roberto Antonino De Siena, Luca 2022-04-25 application/pdf http://rid.unrn.edu.ar/handle/20.500.12049/9346 https://hdl.handle.net/20.500.12049/9346 https://www.frontiersin.org/articles/10.3389/feart.2022.849152/full https://doi.org/10.3389/feart.2022.849152 en eng Frontiers https://www.frontiersin.org/journals/earth-science 10 Frontiers in Earth Science 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 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/4.0/ CC-BY-NC-SA Ciencias Exactas y Naturales Seismic Tomography Data Processing Big Data Volcano Imaging Active Seismicity Data Cleaning 2022 ftunivnrionegro https://doi.org/20.500.12049/9346 https://doi.org/10.3389/feart.2022.849152 2023-01-24T14:43:49Z 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 ... Other/Unknown Material Antarc* Antarctica Deception Island RID-UnRN - Repositorio Institucional Digital Universidad Nacional de Río Negro Argentina Deception Island ENVELOPE(-60.633,-60.633,-62.950,-62.950) Island Bay ENVELOPE(-109.085,-109.085,59.534,59.534) |
institution |
Open Polar |
collection |
RID-UnRN - Repositorio Institucional Digital Universidad Nacional de Río Negro |
op_collection_id |
ftunivnrionegro |
language |
English |
topic |
Ciencias Exactas y Naturales Seismic Tomography Data Processing Big Data Volcano Imaging Active Seismicity Data Cleaning |
spellingShingle |
Ciencias Exactas y Naturales Seismic Tomography Data Processing Big Data Volcano Imaging Active Seismicity Data Cleaning Guardo, Roberto Antonino De Siena, Luca Semi-Automated Inversion-Specific Data Selection for Volcano Tomography |
topic_facet |
Ciencias Exactas y Naturales Seismic Tomography Data Processing Big Data Volcano Imaging Active Seismicity Data Cleaning |
description |
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 ... |
author |
Guardo, Roberto Antonino De Siena, Luca |
author_facet |
Guardo, Roberto Antonino De Siena, Luca |
author_sort |
Guardo, Roberto Antonino |
title |
Semi-Automated Inversion-Specific Data Selection for Volcano Tomography |
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 |
publisher |
Frontiers |
publishDate |
2022 |
url |
http://rid.unrn.edu.ar/handle/20.500.12049/9346 https://hdl.handle.net/20.500.12049/9346 https://www.frontiersin.org/articles/10.3389/feart.2022.849152/full https://doi.org/10.3389/feart.2022.849152 |
long_lat |
ENVELOPE(-60.633,-60.633,-62.950,-62.950) ENVELOPE(-109.085,-109.085,59.534,59.534) |
geographic |
Argentina Deception Island Island Bay |
geographic_facet |
Argentina Deception Island Island Bay |
genre |
Antarc* Antarctica Deception Island |
genre_facet |
Antarc* Antarctica Deception Island |
op_relation |
https://www.frontiersin.org/journals/earth-science 10 Frontiers in Earth Science 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 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/4.0/ |
op_rightsnorm |
CC-BY-NC-SA |
op_doi |
https://doi.org/20.500.12049/9346 https://doi.org/10.3389/feart.2022.849152 |
_version_ |
1766091673495928832 |