TY - JOUR
T1 - Seismic Time-Frequency Analysis via Adaptive Mode Separation-Based Wavelet Transform
AU - Li, Fangyu
AU - Wu, Bangyu
AU - Liu, Naihao
AU - Hu, Ying
AU - Wu, Hao
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral mode separation and an adaptive wavelet bank design. The proposed adaptive mode separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.
AB - To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral mode separation and an adaptive wavelet bank design. The proposed adaptive mode separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.
KW - Adaptive spectral segmentation
KW - optimal mode decomposition
KW - wavelet transform
UR - https://www.scopus.com/pages/publications/85082885497
U2 - 10.1109/LGRS.2019.2930583
DO - 10.1109/LGRS.2019.2930583
M3 - 文章
AN - SCOPUS:85082885497
SN - 1545-598X
VL - 17
SP - 696
EP - 700
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 4
M1 - 8793179
ER -