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The accuracy of gravimetric measurements is essential in various fields, from the safety of the navigation of unmanned autonomous vessels to searching for natural resources to the level of underground water to the accuracy of geodetic data. Usually, we have to deal with measurements contaminated by environmental noise, as well as noise generated by different mechanical devices, city transportation systems, and human beings; some of those sources have a periodical nature. In the presented article, we consider the problem of the influence of noise on registered data. A gravimeter is, in fact, a vibration analyzer, so most of the artificial noise caused by engines, machines, and other technical systems is included in the final recorded data. By testing a statistical hypothesis, we try to convince the reader that in recorded time series, there is other information that is deterministic in nature and may have an important impact on the analysis of gravimetric data.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
150--156
Opis fizyczny
Bibliogr. 29 poz., rys.
Twórcy
autor
- Polish Naval Academy, Gdynia, Poland
autor
- Polish Naval Academy, Gdynia, Poland
autor
- Polish Naval Academy, Gdynia, Poland
autor
- Gdańsk University of Technology, Gdańsk, Poland
autor
- Gdańsk University of Technology, Gdańsk, Poland
autor
- Gdańsk University of Technology, Gdańsk, Poland
autor
- Gdańsk University of Technology, Gdańsk, Poland
Bibliografia
- 1. R. J. Warburton, H. Pillai, and R. C. Reineman, “INITIAL RESULTS WITH THE NEW GWR iGravTM SUPERCONDUCTING GRAVITY METER,” 2010.
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- 6. Y. Kim, S. Lee, K. Okino, and K. Koizumi, “Gravity anomaly across the Yap Trench, Sorol Trough, and southernmost Parece Vela Basin and its implications for the flexural deformation of the lithosphere and regional isostasy,” in AGU Fall Meeting Abstracts, 2005.
- 7. S. Jin and R. Barzaghi, IGFS 2014: Proceedings of the 3rd International Gravity Field Service (IGFS), Shanghai, China, June 30-July 6, 2014, Vol. 144. Springer, 2017.
- 8. Y. L. Smoller et al., “Using airborne gravimeter GT2A in polar areas,” in TG-SMM 2013 - IAG Symposium on Terrestrial Gravimetry: Static and Mobile Measurements, Proceedings, State Research Center of the Russian Federation, 2013, pp. 36–40.
- 9. M. Przyborski, J. Pyrchla, K. Pyrchla, and J. Szulwic, “Microgal gravity measurements with mgs-6 micro-g lacoste gravimeter,” Sensors (Switzerland), Vol. 19, no. 11, 2019, doi: 10.3390/s19112592.
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- 11. L. Lacoste, N. Clarkson, and G. Hamilton, “Lacoste and Romberg Stabilized Platform Shipboard Gravity Meter,” Geophysics, Vol. 32, no. 1, pp. 99–109, 1967, doi: 10.1190/1.1439860.
- 12. L. LaCoste, “LaCoste and Romberg straight-line gravity meter,” Geophysics, Vol. 48, no. 5.
- 13. H. Lyu, S. Wang, X. Zhang, Z. Yang, and M. Pecht, “Reliability modeling for dependent competing failure processes with phase-type distribution considering changing degradation rate,” Eksploatacja i Niezawodność – Maintenance and Reliability, Vol. 23, no. 4, pp. 627–635, Dec. 2021, doi: 10.17531/ein.2021.4.5.
- 14. Z. Zheng, J. Yang, Y. Hu, and X. Wang, “Open-source Software Reliability Modeling with Stochastic Impulsive Differential Equations,” Eksploatacja i Niezawodność – Maintenance and Reliability, Vol. 25, no. 2, p. 2023, 2023, doi: 10.17531/EIN/166342.
- 15. L. A. Rodríguez-Picón, L. C. Méndez-González, I. J. Pérez-Olguín, and J. I. Hernández-Hernández, “A study of the Inverse Gaussian Process with hazard rate functions-based drifts applied to degradation modelling,” Eksploatacja i Niezawodność, Vol. Vol. 24, no. 3, pp. 590–602, 2022, doi: 10.17531/EIN.2022.3.20.
- 16. H. Kantz and T. Schreiber, “Nonlinear Time Series Analysis Nonlinear Time Series Analysis ( 2nd ed. ), by Holger Kantz and Thomas Schreiber, Cambridge, U.K. : Cambridge University Press, 2004, ISBN 0-521-82150-9, xvi + 369 pp., $120.00 .,” Technometrics, Vol. 47, no. 3, pp. 381–381, 2005, Accessed: May 09, 2025. [Online]. Available: http://pubs.amstat.org/doi/abs/10.1198/tech.2005.s306.
- 17. T. Schreiber and H. Kantz, “Noise in chaotic data: Diagnosis and treatment,” Chaos, Vol. 5, no. 1, 1995, doi: 10.1063/1.166095.
- 18. T. Schreiber and A. Schmitz, “Improved surrogate data for nonlinearity tests,” Phys Rev Lett, Vol. 77, no. 4, pp. 635–638, Jul. 1996, doi: 10.1103/PhysRevLett.77.635.
- 19. Y. Lyu, Q. Zhang, A. Chen, and Z. Wen, “Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation,” Eksploatacja i Niezawodność – Maintenance and Reliability, Vol. 25, no. 2, p. 2023, Apr. 2023, doi: 10.17531/EIN/165811.
- 20. J. Kang, Y. Lu, B. Zhao, H. Luo, J. Meng, and Y. Zhang, “Remaining useful life prediction of cylinder liner based on nonlinear degradation model,” Eksploatacja i Niezawodność, vol. Vol. 24, no. 1, pp. 62–69, 2022, doi: 10.17531/EIN.2022.1.8.
- 21. H. D. I. Abarbanel, R. Brown, J. J. Sidorowich, and L. S. Tsimring, “The analysis of observed chaotic data in physical systems,” Rev Mod Phys, Vol. 65, no. 4, p. 1331, Oct. 1993, doi: 10.1103/RevModPhys.65.1331.
- 22. V. V Adushkin, A. A. Spivak, and V. A. Kharlamov, “Effects of lunar-solar tides in the variations of geophysical fields at the boundary between the Earth’s crust and the atmosphere,” Izvestiya, Physics of the Solid Earth, Vol. 48, no. 2, pp. 104–116, 2012.
- 23. T. Gautama, D. P. Mandic, and M. M. Van Hulle, “A Novel Method for Determining the Nature of Time Series,” IEEE Trans Biomed Eng, Vol. 51, no. 5, pp. 728–736, May 2004, doi: 10.1109/TBME.2004.824122.
- 24. F. K, “‘Introduction to Statistical Pattern Recognition, Second Edition’ (Computer Science and Scientific Computing Series),” 1990, Accessed: May 09, 2025. [Online]. Available: http://www.citeulike.org/user/teesid/article/1606649.
- 25. R. Hegger, H. Kantz, and T. Schreiber, “Practical implementation of nonlinear time series methods: The TISEAN package.,” Chaos, Vol. 9, no. 2, pp. 413–435, Jun. 1999, doi: 10.1063/1.166424.
- 26. T. Schreiber, “Interdisciplinary application of nonlinear time series methods,” Phys Rep, vol. 308, no. 1, pp. 1–64, Jan. 1999, doi: 10.1016/S0370-1573(98)00035-0.
- 27. T. Schreiber, “Detecting and Analyzing Nonstationarity in a Time Series Using Nonlinear Cross Predictions,” Phys Rev Lett, vol. 78, no. 5, p. 843, Feb. 1997, doi: 10.1103/PhysRevLett.78.843.
- 28. T. Gautama, D. P. Mandic, and M. M. VanHulle, “A Novel Method for Determining the Nature of Time Series,” IEEE Trans Biomed Eng, vol. 51, no. 5, pp. 728–736, May 2004, doi: 10.1109/TBME.2004.824122.
- 29. J. Theiler, S. Eubank, A. Longtin, B. Galdrikian, and J. Doyne Farmer, “Testing for nonlinearity in time series: the method of surrogate data,” Physica D, vol. 58, no. 1–4, pp. 77–94, Sep. 1992, doi: 10.1016/0167-2789(92)90102-S.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-be9a21e1-0d34-4f7c-8b78-bbc960ac2de0
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