Event detection using a fast matched filter algorithm – An efficient way to deal with big microseismic data sets

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Event detection using a fast matched filter algorithm – An efficient way to deal with big microseismic data setsAuthors: Hanh BuiMirko van der BaanHanh BuiUniversity of AlbertaSearch for more papers by this author and Mirko van der BaanUniversity of AlbertaSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3420645.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractEvent detection is one of the time-consuming parts in microseismic processing. Different automated event detection algorithms have been proposed, such as the short-time average over the long-time average (STA/LTA), power spectral density (PSD), and subspace detection (SD). However, these approaches are not convenient for big data sets. In this study, we introduce a fast matched filter (MF) algorithm, which can solve the efficiency challenge for these detectors. The proposed fast MF is built based on a fast normalized cross-correlation (NCC) technique. This method detects events in the data based on their similarity with template events by comparing the NCC coefficients between the template events and the data with a specific user-defined threshold. The detection workflow consists of six steps, namely data preconditioning, selecting template waveforms, multiplexing, fast NCC computation, extracting potential events, and quality control of the detection results. We have implemented the MF algorithm on a microseismic data set with over 19,000 events detected on two monitoring wells. The MF detection results are compared with the results from STA/LTA. The MF algorithm is more efficient in event detection with fewer false alarms, higher detection probability, and shorter processing time than STA/LTA, especially when dealing with big, noisy data sets.Presentation Date: Monday, October 12, 2020Session Start Time: 1:50 PMPresentation Time: 4:20 PMLocation: 360APresentation Type: OralKeywords: microseismic, hydraulic fracturing, processing, algorithm, crosscorrelationPermalink: https://doi.org/10.1190/segam2020-3420645.1FiguresReferencesRelatedDetailsCited byComparison of sparse Gabor-based methods for detection of microseismic eventsHanh Bui, Mirko van der Baan, and Mauricio D. Sacchi22 February 2023 | GEOPHYSICS, Vol. 88, No. 2Combining Deep Learning and the Source-Scanning Algorithm for Improved Seismic Monitoring23 June 2022 | Bulletin of the Seismological Society of AmericaTime-frequency sparse Gabor transform for detecting microseismic eventsHanh Bui, Mauricio D. Sacchi, and Mirko van der Baan1 September 2021Moment-tensor inversion of hydraulic fracturing-induced events in a Montney reservoir, Northeastern British ColumbiaHanh Bui and Mirko van der Baan1 September 2021 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Hanh Bui and Mirko van der Baan, (2020), "Event detection using a fast matched filter algorithm – An efficient way to deal with big microseismic data sets," SEG Technical Program Expanded Abstracts : 1325-1329. https://doi.org/10.1190/segam2020-3420645.1 Plain-Language Summary Keywordsmicroseismichydraulic fracturingprocessingalgorithmcrosscorrelationPDF DownloadLoading ...