Application of probabilistic power spectral density technique to monitoring the long-term vibrational behaviour of cern seismic network stations

• Autorzy: Łacny Łukasz , Ścisło Łukasz , Guinchard Michael

Identyfikatory

DOI

10.21008/j.0860-6897.2020.3.11

• Języki publikacji: EN

Abstrakty

EN

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).

Słowa kluczowe

EN

ground motion; power spectral density; seismic network

PL

ruch podłoża; gęstość widmowa mocy; sieć sejsmiczna

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2020
• Tom: Vol. 31, nr 3
• Strony: art. no. 2020311
• Opis fizyczny: Bibliogr. 11 poz., 1 fot. kolor., wykr.

Twórcy

autor

Łacny Łukasz

• Cracow University of Technology, Faculty of Mechanical Engineering, Institute of Applied Mechanics, Al. Jana Pawla II 37, 31-864 Cracow, Poland

autor

Ścisło Łukasz

• 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

Guinchard Michael

• 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).