Signal enhancement based on complex curvelet transform and complementary ensemble empirical mode decomposition
| Autor: | Lieqian Dong, Deying Wang, Yimeng Zhang, Datong Zhou |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Data processing 010504 meteorology & atmospheric sciences Series (mathematics) business.industry Noise (signal processing) Speech recognition Mode (statistics) Function (mathematics) 010502 geochemistry & geophysics 01 natural sciences Signal Field (computer science) Hilbert–Huang transform Geophysics business Algorithm 0105 earth and related environmental sciences |
| Zdroj: | Journal of Applied Geophysics. 144:144-150 |
| ISSN: | 0926-9851 |
| DOI: | 10.1016/j.jappgeo.2017.05.004 |
| Popis: | Signal enhancement is a necessary step in seismic data processing. In this paper we utilize the complementary ensemble empirical mode decomposition (CEEMD) and complex curvelet transform (CCT) methods to separate signal from random noise further to improve the signal to noise (S/N) ratio. Firstly, the original data with noise is decomposed into a series of intrinsic mode function (IMF) profiles with the aid of CEEMD. Then the IMFs with noise are transformed into CCT domain. By choosing different thresholds which are based on the noise level difference of each IMF profile, the noise in original data can be suppressed. Finally, we illustrate the effectiveness of the approach by simulated and field datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect