Seismic signal de-noising using time–frequency peak filtering based on empirical wavelet transform

Abstract

Seismic noise suppression plays an important role in seismic data processing and interpretation. The time–frequency peak filtering (TFPF) is a classical method for seismic noise attenuation defined in the time–frequency domain. Nevertheless, we obtain serious attenuation for the seismic signal amplitude when choosing a wide window of TFPF. It is an unsolved issue for TFPF to select a suitable window width for attenuating seismic noise effectively and preserving valid signal amplitude effectively. To overcome the disadvantage of TFPF, we introduce the empirical wavelet transform (EWT) to improve the filtered results produced by TFPF. We name the proposed seismic de-noising workflow as the TFPF based on EWT (TFPF-EWT). We first introduce EWT to decompose a non-stationary seismic trace into a couple of intrinsic mode functions (IMFs) with different dominant frequencies. Then, we apply TFPF to the chosen IMFs for noise attenuation, which are selected by using a defined reference formula. At last, we add the filtered IMFs and the unprocessed ones to obtain the filtered seismic signal. Synthetic data and 3D field data examples prove the validity and effectiveness of the TFPF-EWT for both attenuating random noise and preserving valid seismic amplitude.