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TitleSeismicity-scanning based on navigated automatic phase-picking
 
AuthorTan, F; Kao, HORCID logo; Nissen, E; Eaton, D
SourceJournal of Geophysical Research, Solid Earth vol. 124, issue 4, 2019 p. 3802-3818, https://doi.org/10.1029/2018JB017050 Open Access logo Open Access
Image
Year2019
Alt SeriesNatural Resources Canada, Contribution Series 20190071
PublisherAmerican Geophysical Union (AGU)
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf (Adobe® Reader®); html
Subjectsgeophysics; Science and Technology; seismology; seismicity; array seismology; earthquakes; earthquake catalogues; earthquake magnitudes; epicentres; hydraulic fracturing; methodology; S-SNAP; geological hazards; earthquake detection; automation; induced seismicity
Illustrationsflow diagrams; location maps; tables; geoscientific sketch maps; profiles; spectra; time series; histograms; graphs
ProgramEnvironmental Geoscience, Shale Gas - induced seismicity
Released2019 03 31
AbstractWe propose a new method, named Seismicity-Scanning based on Navigated Automatic Phase-picking (S-SNAP), that is capable of delineating complex spatiotemporal distributions of seismicity. This novel algorithm takes a cocktail approach that combines source scanning, kurtosis-based phase picking, and the maximum intersection location technique into a single integrated workflow. This method is automated, detecting and locating earthquakes efficiently, comprehensively, and accurately. We apply S-SNAP to a data set recorded by a dense local seismic array during a hydraulic fracturing operation to test this novel approach and to demonstrate its effectiveness in relation to existing methods. Overall, S-SNAP found about 3.5 times as many high-quality events as a template matching-based catalogue. All events in the previous catalogue are identified with similar epicenters, depths, and magnitudes, while no false detections are found by visual inspection.
Summary(Plain Language Summary, not published)
We propose a new method capable of delineating complex distributions of earthquakes that occurred in close time and space. This novel algorithm takes a cocktail approach that combines the merits of several previously existing methods into a single integrated workflow. This method is automatic, efficiently providing earthquake locations with high comprehensiveness and accuracy. A test example using data recorded by a dense local seismic array during a hydraulic fracturing operation is presented to demonstrate the advantage of our proposed method.
GEOSCAN ID314699

 
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