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

Autores
Velis, Danilo Rubén; 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.
Facultad de Ciencias Astronómicas y Geofísicas
Materia
Astronomía
Microseismic
Automatic event detection
Denoising
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/99572

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spelling Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filteringVelis, Danilo RubénSabbione, Juan IgnacioSacchi, Mauricio D.AstronomíaMicroseismicAutomatic event detectionDenoisingWe 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.Facultad de Ciencias Astronómicas y Geofísicas2015-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf25-38http://sedici.unlp.edu.ar/handle/10915/99572enginfo:eu-repo/semantics/altIdentifier/url/https://ri.conicet.gov.ar/11336/53691info:eu-repo/semantics/altIdentifier/url/https://library.seg.org/doi/abs/10.1190/geo2014-0561.1info:eu-repo/semantics/altIdentifier/issn/1942-2156info:eu-repo/semantics/altIdentifier/doi/10.1190/GEO2014-0561.1info:eu-repo/semantics/altIdentifier/hdl/11336/53691info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:12:04Zoai:sedici.unlp.edu.ar:10915/99572Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:12:04.743SEDICI (UNLP) - Universidad Nacional de La Platafalse
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 Rubén
Astronomía
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 Rubén
Sabbione, Juan Ignacio
Sacchi, Mauricio D.
author Velis, Danilo Rubén
author_facet Velis, Danilo Rubén
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 Astronomía
Microseismic
Automatic event detection
Denoising
topic Astronomía
Microseismic
Automatic event detection
Denoising
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.
Facultad de Ciencias Astronómicas y Geofísicas
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
Articulo
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info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/99572
url http://sedici.unlp.edu.ar/handle/10915/99572
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ri.conicet.gov.ar/11336/53691
info:eu-repo/semantics/altIdentifier/url/https://library.seg.org/doi/abs/10.1190/geo2014-0561.1
info:eu-repo/semantics/altIdentifier/issn/1942-2156
info:eu-repo/semantics/altIdentifier/doi/10.1190/GEO2014-0561.1
info:eu-repo/semantics/altIdentifier/hdl/11336/53691
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
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