Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering

Autores
Velis, Danilo Ruben; Sabbione, Juan Ignacio; Sacchi, Mauricio D.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We have developed a fast method that allowed us to automatically detect and denoise microseismic phase arrivals from 3C multichannel data. The method is a two-step process. First, the detection is carried out by means of a pattern recognition strategy that seeks plausible hyperbolic phase arrivals immersed in noisy 3C multichannel data. Then, the microseismic phase arrivals are denoised and reconstructed using a reduced-rank approximation of the singular value decomposition of the data along the detected phase arrivals in the context of a deflation procedure that took into account multiple arrivals and/or phases. For the detection, we have defined an objective function that measured the energy and coherence of a potential microseismic phase arrival along an apex-shifted hyperbolic search window. The objective function, which was maximized using very fast simulated annealing, was based on the energy of the average signal and depended on the source position, receivers geometry, and velocity. In practice, the detection process did not require any a priori velocity model, leading to a fast algorithm that can be used in real time, even when the underlying velocity model was not constant. The reduced-rank filtering coupled with a crosscorrelation-based synchronization strategy allowed us to extract the most representative waveform for all the individual traces. Tests using synthetic and field data have determined the reliability and effectiveness of the proposed method for the accurate detection and denoising of 3C multichannel microseismic events under noisy conditions. Two confidence indicators to assess the presence of an actual phase arrival and the reliability of the denoised individual wave arrivals were also developed.
Fil: Velis, Danilo Ruben. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Alberta; Canadá
Fil: Sacchi, Mauricio D.. University of Alberta; Canadá
Materia
Microseismic
Automatic event detection
Denoising
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/53691

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spelling Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filteringVelis, Danilo RubenSabbione, Juan IgnacioSacchi, Mauricio D.MicroseismicAutomatic event detectionDenoisinghttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1We have developed a fast method that allowed us to automatically detect and denoise microseismic phase arrivals from 3C multichannel data. The method is a two-step process. First, the detection is carried out by means of a pattern recognition strategy that seeks plausible hyperbolic phase arrivals immersed in noisy 3C multichannel data. Then, the microseismic phase arrivals are denoised and reconstructed using a reduced-rank approximation of the singular value decomposition of the data along the detected phase arrivals in the context of a deflation procedure that took into account multiple arrivals and/or phases. For the detection, we have defined an objective function that measured the energy and coherence of a potential microseismic phase arrival along an apex-shifted hyperbolic search window. The objective function, which was maximized using very fast simulated annealing, was based on the energy of the average signal and depended on the source position, receivers geometry, and velocity. In practice, the detection process did not require any a priori velocity model, leading to a fast algorithm that can be used in real time, even when the underlying velocity model was not constant. The reduced-rank filtering coupled with a crosscorrelation-based synchronization strategy allowed us to extract the most representative waveform for all the individual traces. Tests using synthetic and field data have determined the reliability and effectiveness of the proposed method for the accurate detection and denoising of 3C multichannel microseismic events under noisy conditions. Two confidence indicators to assess the presence of an actual phase arrival and the reliability of the denoised individual wave arrivals were also developed.Fil: Velis, Danilo Ruben. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Alberta; CanadáFil: Sacchi, Mauricio D.. University of Alberta; CanadáSociety of Exploration Geophysicists2015-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/53691Velis, Danilo Ruben; Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering; Society of Exploration Geophysicists; Geophysics; 80; 6; 7-2015; WC25-WC380016-80331942-2156CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://library.seg.org/doi/abs/10.1190/geo2014-0561.1info:eu-repo/semantics/altIdentifier/doi/10.1190/GEO2014-0561.1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-05T10:35:54Zoai:ri.conicet.gov.ar:11336/53691instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-05 10:35:54.632CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
title Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
spellingShingle Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
Velis, Danilo Ruben
Microseismic
Automatic event detection
Denoising
title_short Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
title_full Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
title_fullStr Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
title_full_unstemmed Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
title_sort Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering
dc.creator.none.fl_str_mv Velis, Danilo Ruben
Sabbione, Juan Ignacio
Sacchi, Mauricio D.
author Velis, Danilo Ruben
author_facet Velis, Danilo Ruben
Sabbione, Juan Ignacio
Sacchi, Mauricio D.
author_role author
author2 Sabbione, Juan Ignacio
Sacchi, Mauricio D.
author2_role author
author
dc.subject.none.fl_str_mv Microseismic
Automatic event detection
Denoising
topic Microseismic
Automatic event detection
Denoising
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We have developed a fast method that allowed us to automatically detect and denoise microseismic phase arrivals from 3C multichannel data. The method is a two-step process. First, the detection is carried out by means of a pattern recognition strategy that seeks plausible hyperbolic phase arrivals immersed in noisy 3C multichannel data. Then, the microseismic phase arrivals are denoised and reconstructed using a reduced-rank approximation of the singular value decomposition of the data along the detected phase arrivals in the context of a deflation procedure that took into account multiple arrivals and/or phases. For the detection, we have defined an objective function that measured the energy and coherence of a potential microseismic phase arrival along an apex-shifted hyperbolic search window. The objective function, which was maximized using very fast simulated annealing, was based on the energy of the average signal and depended on the source position, receivers geometry, and velocity. In practice, the detection process did not require any a priori velocity model, leading to a fast algorithm that can be used in real time, even when the underlying velocity model was not constant. The reduced-rank filtering coupled with a crosscorrelation-based synchronization strategy allowed us to extract the most representative waveform for all the individual traces. Tests using synthetic and field data have determined the reliability and effectiveness of the proposed method for the accurate detection and denoising of 3C multichannel microseismic events under noisy conditions. Two confidence indicators to assess the presence of an actual phase arrival and the reliability of the denoised individual wave arrivals were also developed.
Fil: Velis, Danilo Ruben. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Alberta; Canadá
Fil: Sacchi, Mauricio D.. University of Alberta; Canadá
description We have developed a fast method that allowed us to automatically detect and denoise microseismic phase arrivals from 3C multichannel data. The method is a two-step process. First, the detection is carried out by means of a pattern recognition strategy that seeks plausible hyperbolic phase arrivals immersed in noisy 3C multichannel data. Then, the microseismic phase arrivals are denoised and reconstructed using a reduced-rank approximation of the singular value decomposition of the data along the detected phase arrivals in the context of a deflation procedure that took into account multiple arrivals and/or phases. For the detection, we have defined an objective function that measured the energy and coherence of a potential microseismic phase arrival along an apex-shifted hyperbolic search window. The objective function, which was maximized using very fast simulated annealing, was based on the energy of the average signal and depended on the source position, receivers geometry, and velocity. In practice, the detection process did not require any a priori velocity model, leading to a fast algorithm that can be used in real time, even when the underlying velocity model was not constant. The reduced-rank filtering coupled with a crosscorrelation-based synchronization strategy allowed us to extract the most representative waveform for all the individual traces. Tests using synthetic and field data have determined the reliability and effectiveness of the proposed method for the accurate detection and denoising of 3C multichannel microseismic events under noisy conditions. Two confidence indicators to assess the presence of an actual phase arrival and the reliability of the denoised individual wave arrivals were also developed.
publishDate 2015
dc.date.none.fl_str_mv 2015-07
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 http://hdl.handle.net/11336/53691
Velis, Danilo Ruben; Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering; Society of Exploration Geophysicists; Geophysics; 80; 6; 7-2015; WC25-WC38
0016-8033
1942-2156
CONICET Digital
CONICET
url http://hdl.handle.net/11336/53691
identifier_str_mv Velis, Danilo Ruben; Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering; Society of Exploration Geophysicists; Geophysics; 80; 6; 7-2015; WC25-WC38
0016-8033
1942-2156
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://library.seg.org/doi/abs/10.1190/geo2014-0561.1
info:eu-repo/semantics/altIdentifier/doi/10.1190/GEO2014-0561.1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Society of Exploration Geophysicists
publisher.none.fl_str_mv Society of Exploration Geophysicists
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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