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
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/53691
Ver los metadatos del registro completo
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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 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
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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 |
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eng |
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Society of Exploration Geophysicists |
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Society of Exploration Geophysicists |
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