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...

Full description

Bibliographic Details
Main Authors: Guardo, Roberto Antonino, De Siena, Luca
Language:English
Published: Frontiers 2022
Subjects:
Online Access: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
id ftunivnrionegro:oai:rid.unrn.edu.ar:20.500.12049/9346
record_format openpolar
spelling 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