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• W1991521938 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2012Novel hybrid ANN autopicker for hydrofrac data: A comparative studyAuthors: Debotyam MaityFred AminzadehMartin KarrenbachDebotyam MaityDepartment of Petroleum Engineering, University of Southern CaliforniaSearch for more papers by this author, Fred AminzadehDepartment of Petroleum Engineering, University of Southern CaliforniaSearch for more papers by this author, and Martin KarrenbachSR2020 Inc.Search for more papers by this authorhttps://doi.org/10.1190/segam2012-0805.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract A wide variety of autopicking algorithms have been developed and are being used to detect phase arrivals from microseismic data. All of the available methods are effective in high SNR (signal-to-noise ratio) environments. However, with the reduction in SNR, there is always a possibility of the arrivals not being detected and classified by standard methods. Different autopicking workflows in use within the academia and the industry show different degrees of performance drop under different scenarios. This is particularly the case in very noisy environments associated with most hydrofrac projects. The quality of the first arrival detection is related to the near- and sub- surface structure, source type, and prevalent SNR conditions. Any of the available autopicking methods could be applicable in some specific scenarios and may fail in others based on the acquisition conditions. Therefore, finding a robust method to work under most circumstances is a major challenge. Moreover, due to large volumes of seismic data common in acquisitions involving passive seismic arrays and the complexity of the autopicking approach in use, the detection of the arrivals can be time consuming. Since detection of the first arrival in a fast and accurate fashion is the key step for additional processing, the aim is to work with data that is characterized by low SNR and poor overall data quality to develop an effective workflow to obtain P-wave and S-wave first arrivals with high accuracy by using combination of attributes in an ANN (artificial neural networks) framework. In this paper, we provide a comparative analysis of the efficacy of different picking algorithms on a synthetic dataset. We benchmark our novel ANN based autopicker with two contemporary autopicking algorithms and validate its applicability for the test case. Permalink: https://doi.org/10.1190/segam2012-0805.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2012ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2012 Pages: 4609 Publisher:Society of Exploration Geophysicists HistoryPublished: 25 Oct 2012 CITATION INFORMATION Debotyam Maity, Fred Aminzadeh, and Martin Karrenbach, (2012), Novel hybrid ANN autopicker for hydrofrac data: A comparative study, SEG Technical Program Expanded Abstracts : 1-5. https://doi.org/10.1190/segam2012-0805.1 Plain-Language Summary PDF DownloadLoading ..." @default.
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• W1991521938 title "Novel hybrid ANN autopicker for hydrofrac data: A comparative study" @default.
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