Radon transform-based microseismic event detection and signal-to-noise ratio enhancement
- Autores
- Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Velis, Danilo Ruben
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- We present an adaptive filtering method to denoise downhole microseismic data. The methodology uses the apex-shifted parabolic Radon transform. The algorithm is implemented in two steps. In the first step we apply the apex-shifted parabolic Radon transform to the normalized root mean square envelope of the microseismic data to detect the presence of an event. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring. The P? and S?wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records.
Fil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Sacchi, Mauricio D.. University of Alberta; Canadá
Fil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina - Materia
-
Microseismic
Denosing
Adaptive Filtering
Radon Transform - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/45978
Ver los metadatos del registro completo
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Radon transform-based microseismic event detection and signal-to-noise ratio enhancementSabbione, Juan IgnacioSacchi, Mauricio D.Velis, Danilo RubenMicroseismicDenosingAdaptive FilteringRadon Transformhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1We present an adaptive filtering method to denoise downhole microseismic data. The methodology uses the apex-shifted parabolic Radon transform. The algorithm is implemented in two steps. In the first step we apply the apex-shifted parabolic Radon transform to the normalized root mean square envelope of the microseismic data to detect the presence of an event. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring. The P? and S?wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records.Fil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Sacchi, Mauricio D.. University of Alberta; CanadáFil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaElsevier Science2015-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/45978Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Velis, Danilo Ruben; Radon transform-based microseismic event detection and signal-to-noise ratio enhancement; Elsevier Science; Journal Of Applied Geophysics; 113; 2-2015; 51-630926-9851CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jappgeo.2014.12.008info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0926985114003619info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:04:56Zoai:ri.conicet.gov.ar:11336/45978instacron: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:34982025-09-29 10:04:57.024CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
title |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
spellingShingle |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement Sabbione, Juan Ignacio Microseismic Denosing Adaptive Filtering Radon Transform |
title_short |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
title_full |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
title_fullStr |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
title_full_unstemmed |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
title_sort |
Radon transform-based microseismic event detection and signal-to-noise ratio enhancement |
dc.creator.none.fl_str_mv |
Sabbione, Juan Ignacio Sacchi, Mauricio D. Velis, Danilo Ruben |
author |
Sabbione, Juan Ignacio |
author_facet |
Sabbione, Juan Ignacio Sacchi, Mauricio D. Velis, Danilo Ruben |
author_role |
author |
author2 |
Sacchi, Mauricio D. Velis, Danilo Ruben |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Microseismic Denosing Adaptive Filtering Radon Transform |
topic |
Microseismic Denosing Adaptive Filtering Radon Transform |
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 present an adaptive filtering method to denoise downhole microseismic data. The methodology uses the apex-shifted parabolic Radon transform. The algorithm is implemented in two steps. In the first step we apply the apex-shifted parabolic Radon transform to the normalized root mean square envelope of the microseismic data to detect the presence of an event. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring. The P? and S?wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records. Fil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina Fil: Sacchi, Mauricio D.. University of Alberta; Canadá Fil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina |
description |
We present an adaptive filtering method to denoise downhole microseismic data. The methodology uses the apex-shifted parabolic Radon transform. The algorithm is implemented in two steps. In the first step we apply the apex-shifted parabolic Radon transform to the normalized root mean square envelope of the microseismic data to detect the presence of an event. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring. The P? and S?wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-02 |
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/11336/45978 Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Velis, Danilo Ruben; Radon transform-based microseismic event detection and signal-to-noise ratio enhancement; Elsevier Science; Journal Of Applied Geophysics; 113; 2-2015; 51-63 0926-9851 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/45978 |
identifier_str_mv |
Sabbione, Juan Ignacio; Sacchi, Mauricio D.; Velis, Danilo Ruben; Radon transform-based microseismic event detection and signal-to-noise ratio enhancement; Elsevier Science; Journal Of Applied Geophysics; 113; 2-2015; 51-63 0926-9851 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jappgeo.2014.12.008 info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0926985114003619 |
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 |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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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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13.070432 |