Application of the Variational-Mode Decomposition for Seismic Time–frequency Analysis
| Autor: | Junxing Cao, Yao Yao, Ya-juan Xue, Da-xing Wang, Hao-kun Du |
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| Rok vydání: | 2016 |
| Předmět: |
Atmospheric Science
Signal processing Speech recognition Bandwidth (signal processing) 0211 other engineering and technologies Short-time Fourier transform Wavelet transform 02 engineering and technology 010502 geochemistry & geophysics 01 natural sciences Hilbert–Huang transform Time–frequency analysis symbols.namesake Fourier transform Robustness (computer science) symbols Computers in Earth Sciences Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9:3821-3831 |
| ISSN: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2016.2529702 |
| Popis: | Seismic time–frequency analysis methods play an important role in seismic interpretation for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. Variational-mode decomposition (VMD) is a newly developed methodology for decomposition on adaptive and quasi-orthogonal signal and can decompose a seismic signal into a number of band-limited quasi-orthogonal intrinsic mode functions (IMFs). Each mode is an AM–FM signal with the narrow-band property and nonnegative smoothly varying instantaneous frequencies. Analysis on synthetic and real data shows that this method is more robust to noise and has stronger local decomposition ability than the empirical mode decomposition (EMD)-based methods. Comparing with the short-time Fourier transform (STFT) or wavelet transform (WT), instantaneous spectrum after VMD promises higher spectral and spatial resolution. Application of the VMD on field data demonstrates that instantaneous spectrum after VMD targets the thickness variation in the coal seam more sensitively than the conventional tools and highlights the fine details that might escape unnoticed. The technique is more promising for seismic signal processing and interpretation. |
| Databáze: | OpenAIRE |
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