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Automatic seismic event detection using migration and stacking: a performance and parameter study in Hengill, southwest Iceland
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Geophysics.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Geophysics.ORCID iD: 0000-0002-2511-187X
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Geophysics.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Geophysics.ORCID iD: 0000-0002-0789-5949
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2017 (English)In: Geophysical Journal International, ISSN 0956-540X, E-ISSN 1365-246X, Vol. 209, no 3, p. 1866-1877Article in journal (Refereed) Published
Abstract [en]

We investigate the performance of a seismic event detection algorithm using migration and stacking of seismic traces. The focus lies on determining optimal data dependent detection parameters for a data set from a temporary network in the volcanically active Hengill area, southwest Iceland. We test variations of the short-term average to long-term average and Kurtosis functions, calculated from filtered seismic traces, as input data. With optimal detection parameters, our algorithm identified 94 per cent (219 events) of the events detected by the South Iceland Lowlands (SIL) system, that is, the automatic system routinely used on Iceland, as well as a further 209 events, previously missed. The assessed number of incorrect (false) detections was 25 per cent for our algorithm, which was considerably better than that from SIL (40 per cent). Empirical tests show that well-functioning processing parameters can be effectively selected based on analysis of small, representative subsections of data. Our migration approach is more computationally expensive than some alternatives, but not prohibitively so, and it appears well suited to analysis of large swarms of low magnitude events with interevent times on the order of seconds. It is, therefore, an attractive, practical tool for monitoring of natural or anthropogenic seismicity related to, for example, volcanoes, drilling or fluid injection.

Place, publisher, year, edition, pages
2017. Vol. 209, no 3, p. 1866-1877
Keywords [en]
Numerical solutions, Time-series analysis, Induced seismicity, Volcano seismology
National Category
Geophysics
Identifiers
URN: urn:nbn:se:uu:diva-359626DOI: 10.1093/gji/ggx127ISI: 000408374300036OAI: oai:DiVA.org:uu-359626DiVA, id: diva2:1246477
Funder
Swedish Research Council, 2008:3754Available from: 2018-09-07 Created: 2018-09-07 Last updated: 2019-02-08Bibliographically approved
In thesis
1. Toward fully automatic earthquake detection and processing for tomography in the Hengill area
Open this publication in new window or tab >>Toward fully automatic earthquake detection and processing for tomography in the Hengill area
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on the automation of seismic data analysis, in particular, event detection, quality assessment of detected events, and preparation of an earthquake catalogue for seismic tomography.

The developed event detector uses back-propagation and stacking of a seismic trace attribute with a known velocity model to detect and locate events. A four-dimensional volume in space and time is probed for local maxima of coherently stacking signals. These local maxima define event location and origin time. Application of the detection algorithm to data from a dense 26-station 3-component seismic network in the Hengill area, SW Iceland, produced an increased true-to-false detection ratio compared to the local detection routine.

The detected events were analysed using inter-event cross-correlation with a manually picked reference catalogue to determine their similarity with real events. Automatic P- and S-phase picks were derived using the time delay information from highly correlated events. Relocation with the determined phase picks improves hypocentre uncertainty. A multi-stage selection process is implemented to categorise the detected events into different classes of varying priority for a potential manual analysis. Depending on the used parameters, the top quality category of events can be used in e.g. local-earthquake tomography without manual inspection. Iterative application of the algorithm improved the reference catalogue by almost 40% with events of at least equal quality.

The final local-earthquake tomography with the updated reference catalogue confirms the success of the implemented workflow. The resulting Vp, Vs, and Vp/Vs models show structures that can be associated with the local geothermal activity. A higher resolution and extended ray coverage was achieved compared to previous tomographic studies. Double-difference location of the events using differential times from waveform correlation significantly improved event hypocentres revealing detailed fault geometry in the known seismicity pattern. A preliminary double-difference tomography shows promising results for high resolution imaging.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 53
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1774
Keywords
Earthquake detection, local-earthquake tomography, Iceland, induced seismicity, geothermal activity
National Category
Geophysics
Research subject
Geophysics with specialization in Seismology
Identifiers
urn:nbn:se:uu:diva-376658 (URN)978-91-513-0574-5 (ISBN)
Public defence
2019-03-28, Hambergsalen, Villavägen 16, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-05 Created: 2019-02-08 Last updated: 2019-03-18

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Wagner, FredericTryggvason, AriRoberts, Roland G.Lund, BjörnGudmundsson, Ólafur

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