Radon transform-based microseismic event detection and signal-to-noise ratio enhancement
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...
| Authors: | ; ; |
|---|---|
| Format: | Article |
| Status: | Published version |
| Publication date: | 2015 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/45978 |
| Online Access: | http://hdl.handle.net/11336/45978 |
| Access Level: | Open access |
| Keyword: | Microseismic Denosing Adaptive Filtering Radon Transform https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| _version_ | 1878403707319615488 |
|---|---|
| author | Sabbione, Juan Ignacio |
| author2 | Sacchi, Mauricio D. Velis, Danilo Ruben |
| author2_role | author author |
| author_browse | Sabbione, Juan Ignacio Sacchi, Mauricio D. Velis, Danilo Ruben |
| author_facet | Sabbione, Juan Ignacio Sacchi, Mauricio D. Velis, Danilo Ruben |
| author_role | author |
| collection | CONICET Digital (CONICET) |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | AR_b1663c7d7e003e2d8b6a1f5b4418b580 |
| 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 |
| institution | Consejo Nacional de Investigaciones Científicas y Técnicas |
| instname_str | Consejo Nacional de Investigaciones Científicas y Técnicas |
| language | eng |
| network_acronym_str | AR |
| network_name_str | Argentina |
| oai_identifier_str | oai:ri.conicet.gov.ar:11336/45978 |
| publishDate | 2015 |
| publishDateSort | 2015 |
| publisher | Elsevier Science |
| publisherStr | Elsevier Science |
| reponame_str | CONICET Digital (CONICET) |
| spelling | Radon transform-based microseismic event detection and signal-to-noise ratio enhancement Sabbione, Juan Ignacio Sacchi, Mauricio D. Velis, Danilo Ruben Microseismic Denosing Adaptive Filtering Radon Transform https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 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 Elsevier Science 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 |
| spellingShingle | Radon transform-based microseismic event detection and signal-to-noise ratio enhancement Sabbione, Juan Ignacio Microseismic Denosing Adaptive Filtering Radon Transform https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| status_str | publishedVersion |
| title | 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_short | 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 |
| topic | Microseismic Denosing Adaptive Filtering Radon Transform https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| topic_facet | Microseismic Denosing Adaptive Filtering Radon Transform https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| url | http://hdl.handle.net/11336/45978 |