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Determination of optimum kernel bandwidth for northern Marmara region, Turkey

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As a relatively recent development, spatial smoothing methods have been introduced to identify seismic patterns. Among the methods developed to model the spatial variation, methods based on utilization of 3-D Gaussian isotropic kernels have a wide reception. The most important question remaining to be answered in the application of these methods is the determination of the optimum kernel bandwidth. At the present, researchers’ efforts to clarify the subject have still not yet finalized, this study aims to provide insightful knowledge for future efforts. In this study, for the region bounded by 27– 33 longitudes and 39–41 latitudes, where the western section of the famous Northern Anatolian fault lies, smoothing techniques are implemented to determine the optimum smoothing kernel bandwidth for point sources. The influence of the modeling of seismicity through the computation of the optimum smoothing kernel bandwidth is examined. In addition, the sensitivity of each smoothing technique to the seismic patterns, whether densely clustered or scarcely populated, is investigated. In the end, the smoothing method based on optimum neighbor number is identified as highly sensitive to the density of seismicity and seismic clusters, and better in modeling high seismicity compared to the model based on single optimum smoothing distance used for the entire region of interest.
Czasopismo
Rocznik
Strony
633--642
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
  • Disaster and Emergency Management Directorate, NiğdeTurkey
Bibliografia
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  • 12. Helmstetter A, Werner JM (2014) Adaptive smoothing of seismicity in time, space and magnitude for time-dependent earthquake forecasts for California. Bull Seismol Soc Am 104(2):809–822
  • 13. Helmstetter A, Kagan YY, Jackson DD (2006) Comparison of short-term and time-independent earthquake forecast models for southern California. Bull Seismol Soc Am 96(1):90–106
  • 14. Helmstetter A, Kagan YY, Jackson DD (2007) High-resolution time-independent grid-based forecast for M ≥ 5 earthquakes in California. Seismol Res Lett 78(1):78–86
  • 15. Hiemer S, Woessner J, Basili R, Danciu L, Giardini D, Wiemer S (2014) A smoothed stochastic earthquake rate model considering seismicity and fault moment release for Europe. Geophys J Int 198:1159–1172
  • 16. Kagan YY (1997) Seismic moment-frequency relation for shallow earthquakes: regional comparison. J Geophys Res 102(B2):2835–2852
  • 17. Kagan YY, Jackson DD (1994) Long-term probabilistic forecasting of earthquakes. J Geophys Res 99(B7):13685–13700
  • 18. Kagan YY, Knopoff L (1977) Earthquake risk prediction as a stochastic process. Phys Earth Planet Int 14(2):97–108
  • 19. Karaca H (2014) Estimation of potential earthquake losses for the evaluation of earthquake insurance risks. Ph.D. Thesis, METU Civil Engineering Department, Ankara, Turkey
  • 20. McGuire RK (1976) Fortran computer program for seismic risk analysis. In: Technical report 2, U.S. Geology Survey. Open-file report 76-67
  • 21. Moschetti MP, Mueller CS, Boyd OS, Petersen MD (2014) Development of an adaptively smoothed seismicity model for Alaska and implications for seismic hazard. In: Tenth U.S. national conference on earthquake engineering, Anchorage, Alaska
  • 22. Stock C, Smith EGC (2002) Adaptive kernel estimation and continuous probability representation of historical earthquake catalogs. Bull Seismol Soc Am 92(3):904–912
  • 23. Werner MJ, Helmstetter A, Jackson DD, Kagan YY (2011) High-resolution long-term and short-term earthquake forecasts for California. Bull Seismol Soc Am 101(4):1630–1648
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1ad79ff1-4ad2-45b2-9b8d-d0715f7f1165
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