一种高阶相速度频散计算方法
By removing aliasing using the CCBF algorithm and combining compressed sensing and wavelet transform techniques, the problems of medium inhomogeneity and noise interference in the CCBF method are solved, and high-precision high-order phase velocity dispersion calculation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSTITUTE OF GEOLOGY AND GEOPHYSICS CHINESE ACADEMY OF SCIENCES
- Filing Date
- 2026-01-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing CCBF methods face challenges in seismic wave imaging, including medium inhomogeneity, aliasing due to multipath effects, data sparsity, and noise interference, making it difficult to achieve high-precision high-order phase velocity dispersion calculations.
By preserving the causal part of the signal and removing aliasing using the CCBF algorithm, non-uniform sampling is performed using compressed sensing theory, and reconstruction is carried out using an iterative shrinking threshold algorithm based on multi-scale mixed wavelet basis functions. Incompressible random noise is also thresholded to obtain high-precision CCBF high-order phase velocity dispersion calculation results.
It significantly improves the resolution and signal-to-noise ratio of dispersion spectra, reduces noise sensitivity, and realizes high-precision CCBF high-order phase velocity dispersion calculation under sparse observation conditions.
Smart Images
Figure CN121918176B_ABST