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In this paper, a statistical method called Probabilistic Power Spectral Density based on the standard spectral density plots is presented and utilized. The practical application and utility of this method are shown based on the seismic data collected over a long period from three seismic stations connected within the so-called CERN Seismic Network. The analysis was used to observe and monitor the increase in ambient vibration levels over a long period during the heightened heavy machinery work close to LHC Point 1 (ATLAS detector).
Czasopismo
Rocznik
Tom
Strony
art. no. 2020311
Opis fizyczny
Bibliogr. 11 poz., 1 fot. kolor., wykr.
Twórcy
autor
- Cracow University of Technology, Faculty of Mechanical Engineering, Institute of Applied Mechanics, Al. Jana Pawla II 37, 31-864 Cracow, Poland
autor
- Cracow University of Technology, Faculty of Electrical and Computer Engineering, Department of Automatic Control and Information Technology, ul. Warszawska 24, 31-155 Cracow, Poland
autor
- CERN, Engineering, Department, EN-MME Group, Mechanical Measurement Lab, 1211 Geneva 23, Switzerland
Bibliografia
- 1. High Luminosity LHC Project, accessed 26 Nov 2020, https://hilumilhc.web.cern.ch
- 2. D. E. McNamara, R. P. Buland, Ambient Noise Levels in the Continental United States, Bulletin of the Seismological Society of America, 94(4) (2004) 1517-1527.
- 3. J. Peterson, Observations and Modeling of Seismic Background Noise, U.S. Geological Survey open-file report 93-322, Albuquerque, N.M., 1993.
- 4. D. E. McNamara, R. I. Boaz, Seismic Noise Analysis System Using Power Spectral Density Probability Density Functions: A Stand-Alone Software Package, US Geological Survey, Resston VI, 2006.
- 5. N. Jana, C. Singh, R. Biswas, N. Grewal, A. Singh, Seismic noise analysis of broadband stations in the Eastern Ghat Mobile Belt of India using power spectral density, Geomatic, Natural Hazards and Risk 8(2) (2017) 1622-1630.
- 6. L. Dimitrova, Noise level on selected digital stations of the National Operative Telemetric System for Seismic information (NOTSSI), Comptes Rendus de l’Academie Bulgare des Sciences: Sciences Mathematiques and Naturales, 62(4) (2009), 515-520.
- 7. T. A. Stabile et al., The INSIEME seismic network: a research infrastructure for studying induced seismicity in the High AgriValley (southern Italy). Earth System Science Data Discussions. (2019) 1-31.
- 8. F. Fuchs et al., Site selection for a countrywide temporary network in Austria: Noise analysis and preliminary performance. Advances in Geosciences. 41. (2015) 25-33.
- 9. L. Krischer, T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, J. Wassermann, ObsPy: a bridge for seismology into the scientific Python ecosystem, Computational Science and Discovery, 8(1) (2015) 014003.
- 10. C. Charrondière, K. Develle, M. Guinchard, M. Cabon, Ground vibration monitoring at CERN as part of the international seismic network, ICALPECS 2017, Barcelona, Spain, 2017.
- 11. International Federation of Digital Seismograph Networks: C4 CERN Seismic Network, accessed 26 Nov 2020, https://www.fdsn.org/networks/detail/C4/.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-be548a69-9eec-40ca-94c3-3627b301214a