METHOD OF EMPIRICAL MODES AND WAVELETFILTERING: APPLICATION IN GEOPHYSICAL PROBLEMS
Cite this article as:
Pavlov A. N., Filatova А. Е. METHOD OF EMPIRICAL MODES AND WAVELETFILTERING: APPLICATION IN GEOPHYSICAL PROBLEMS. Izvestiya VUZ. Applied Nonlinear Dynamics, 2011, vol. 19, iss. 1, pp. 3-13. DOI: https://doi.org/10.18500/0869-6632-2011-19-1-3-13
Theoretical bases of empirical mode decomposition being one of the new methods of timefrequency analysis of processes with timevarying characteristics are discussed. It is shown that application of this approach together with waveletfiltering allows one to study in details the structure of multicomponent registered signals recorded in prospecting seismology.
1. Peng C.-K., Havlin S., Stanley H., Goldberger A. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series // Chaos. 1995. Vol. 5. P. 82.
2. Muzy J.F., Bacry E., Arneodo A. The multifractal formalism revisited with wavelets // Int. J. Bifurcation and Chaos. 1994. Vol. 4, No 2. P. 245.
3. Daubechies I. Ten lectures on wavelets. Philadelphia: S.I.A.M., 1992.
4. Meyer Y. Wavelets: Algorithms and applications. Philadelphia: S.I.A.M., 1993.
5. Mallat S.G. A wavelet tour of signal processing. New York: Academic Press, 1998.
6. Addison P.S. The illustrated wavelet transform handbook: applications in science, engineering, medicine and finance. Bristol; Philadelphia: IOP Publishing, 2002.
7. Flandrin P. Some aspects of non-stationary signal processing with emphasis on time-frequency and time-scale methods // Wavelets / Eds J.M. Combes, A. Grossmann, Ph. Tchamitchian. Springer, Berlin. 1989. P. 68.
8. Flandrin P. Time-frequency and time-scale analysis. San Diego: Academic Press, 1999.
9. Анисимов А.А., Павлова О.Н., Тупицын А.Н., Павлов А.Н. Вейвлет-анализ чирпов // Изв. вузов. Прикладная нелинейная динамика. 2008. Т. 16, No 5. С. 3.
10. Kaiser G. A friendly guide to wavelets. Boston: Birkhauser, 1994. ̈
11. Короновский А.А., Храмов А.Е. Непрерывный вейвлетный анализ и его приложения. М.: Физматлит, 2003.
12. Zhang Q., Benveniste A. Wavelet networks // IEEE Trans. Neural Networks. 1992. Vol. 3. P. 889.
13. Zhang J., Walter G.G., Miao Y., Lee W.N. Wavelet neural networks for function learning // IEEE Trans. Signal Proc. 1995. Vo1. 43. P. 1485.
14. Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N.-C., Tung C.C. and Liu H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis // Proc. R. Soc. London Ser. A. 1998. Vol. 454. C. 903.
15. Coughlin K.T., Tung K.K. 11-year solar cycle in the stratosphere extracted by the empirical mode decomposition method // Adv. Space Res. 2004. Vol. 34. P. 39.
16. Neto E.P.S., Custaud M.A., Cejka C.J., Abry P., Frutoso J., Gharib C., Flandrin P. Assessment of cardiovascular autonomic control by the empirical mode decomposition // Method. Inform. Med. 2004. Vol. 43. P. 60.
17. Wu Z., Huang N.E. A study of the characteristics of white noise using the empirical mode decomposition method // Proc. R. Soc. London, Ser. A. 2004. Vol. 460. P. 1597.
18. Huang N.E., Shen Z., Long S.R. A new view of nonlinear water waves: the Hilbert spectrum // Annu. Rev. Fluid Mech. 1999. Vol. 31. C. 417.
19. Hilbert–Huang transform and its applications / Eds N.E. Huang, S.S.P. Shen. Singapore: World Scientific, 2005.
20. Flandrin P., Goncalves P. ́ Empirical mode decompositions as data-driven wavelet-like expansion // Int. J. Wavelets Multiresolut. Inform. Process. 2004. Vol. 2. P. 477.
21. Flandrin P., Rilling G., Goncalves P. ́ Empirical mode decompositions as a filter bank // IEEE Signal Process. Lett. 2004. Vol. 11. P. 112.
22. Филатова А.Е., Артемьев А.Е., Короновский А.А., Павлов А.Н., Храмов А.Е. Успехи и перспективы применения вейвлетных преобразований для анализа нестационарных нелинейных данных в современной геофизике // Известия вузов. Прикладная нелинейная динамика. 2010. Т. 18, No 3. С. 3.
BibTeX
author = {A. N. Pavlov and А. Е. Filatova},
title = {METHOD OF EMPIRICAL MODES AND WAVELETFILTERING: APPLICATION IN GEOPHYSICAL PROBLEMS},
year = {2011},
journal = {Izvestiya VUZ. Applied Nonlinear Dynamics},
volume = {19},number = {1},
url = {https://old-andjournal.sgu.ru/en/articles/method-of-empirical-modes-and-waveletfiltering-application-in-geophysical-problems},
address = {Саратов},
language = {russian},
doi = {10.18500/0869-6632-2011-19-1-3-13},pages = {3--13},issn = {0869-6632},
keywords = {Instantaneous frequency,empirical mode decomposition,Waveletanalysis,seismic prospecting.},
abstract = { Theoretical bases of empirical mode decomposition being one of the new methods of timefrequency analysis of processes with timevarying characteristics are discussed. It is shown that application of this approach together with waveletfiltering allows one to study in details the structure of multicomponent registered signals recorded in prospecting seismology. }}