Fluvial channel characterization using the improved empirical wavelet transform

The empirical wavelet transform (EWT) builds an adaptive filter bank and decomposes an analyzed signal into several intrinsic mode functions (IMFs). Although some applications have certified the effectiveness of the EWT, the EWT becomes invalid when analyzing non-stationary signals (e.g. seismic signals). In this paper, we propose an improved empirical wavelet transform (IEWT) to decompose a seismic signal into several IMFs and describe its frequency features. After computing the Fourier spectrum of the analyzed seismic signal, we first implement the scale-space representation (SSR) to the Fourier spectrum. Then, we obtain an adaptive spectrum segmentation using detected boundaries based on the SSR. Afterward, the proposed algorithm obtains accurate and stable IMFs in decomposing a nonstationary seismic signal. To demonstrate the effectiveness of the proposed IEWT, we apply it to synthetic seismogram and field data.