Seismic Time–Frequency Analysis via Empirical Wavelet Transform

TLDR

Time‑frequency analysis reveals hidden information in seismic data, and its high‑resolution representations are crucial for depicting geological structures; the empirical wavelet transform (EWT), a fully adaptive signal‑analysis method related to empirical mode decomposition, offers a consolidated mathematical framework for such analysis. The study proposes a novel seismic time‑frequency analysis approach that employs the newly developed empirical wavelet transform (EWT). The method first estimates frequency components in the seismic signal, computes their boundaries, and extracts oscillatory components accordingly, and its effectiveness is demonstrated on synthetic, 2‑D, and 3‑D real seismic data. This is the first application of EWT to multichannel seismic data, and the results demonstrate that it delivers higher resolution than the traditional continuous wavelet transform, enabling precise highlighting of geological and stratigraphic features.

Abstract

Time-frequency analysis is able to reveal the useful information hidden in the seismic data. The high resolution of the time-frequency representation is of great importance to depict geological structures. In this letter, we propose a novel seismic time-frequency analysis approach using the newly developed empirical wavelet transform (EWT). It is the first time that EWT is applied in analyzing multichannel seismic data for the purpose of seismic exploration. EWT is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition but has a consolidated mathematical background. EWT first estimates the frequency components presented in the seismic signal, then computes the boundaries, and extracts oscillatory components based on the boundaries computed. Synthetic, 2-D, and 3-D real seismic data are used to comprehensively demonstrate the effectiveness of the proposed seismic time-frequency analysis approach. Results show that the EWT can provide a much higher resolution than the traditional continuous wavelet transform and offers the potential in precisely highlighting geological and stratigraphic information.