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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/99572
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 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 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://sedici.unlp.edu.ar/handle/10915/99572 |
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http://sedici.unlp.edu.ar/handle/10915/99572 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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