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

Liu Naihao, Yang Yang, Li Zhen, Gao Jinghuai, Jiang Xiudi, Pan Shulin

Acta Geophysica

2020

Vol. 68, no. 2

425--434

Seismic noise suppression plays an important role in seismic data processing and interpretation. The time–frequency peak fltering (TFPF) is a classical method for seismic noise attenuation defned 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 efectively and preserving valid signal amplitude efectively. To overcome the disadvantage of TFPF, we introduce the empirical wavelet transform (EWT) to improve the fltered results produced by TFPF. We name the proposed seismic de-noising workfow as the TFPF based on EWT (TFPFEWT). We frst introduce EWT to decompose a non-stationary seismic trace into a couple of intrinsic mode functions (IMFs) with diferent dominant frequencies. Then, we apply TFPF to the chosen IMFs for noise attenuation, which are selected by using a defned reference formula. At last, we add the fltered IMFs and the unprocessed ones to obtain the fltered seismic signal. Synthetic data and 3D feld data examples prove the validity and efectiveness of the TFPF-EWT for both attenuating random noise and preserving valid seismic amplitude.

Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering

Yu P., Li Y., Lin H., Wu N.

Acta Geophysica

2016

Vol. 64, no. 5

1703--1714

Time-frequency peak filtering (TFPF) is an effective tool for the removal of random noise and can be used to process seismic data with a low signal-to-noise ratio. A crucial aspect of this algorithm is the choice of window length (WL) of the time-frequency distribution. Whereas a fixed WL cannot simultaneously preserve signal and attenuate noise, timevarying WLs can achieve this goal. We propose a new method, L-DVV (delay vector variance), which successfully processes non-stationary signals by using the surrogate to measure the non-linearity of a time series. This method is sensitive to random noise and can accurately recover seismic signal masked by noise. Since the linearity criterion also meets the unbiased estimation criterion of the TFPF algorithm, the L-DVV method can be used for time-varying WL TFPF processing. Analysis of synthetic and real seismic data shows that the time-varying WL TFPF algorithm is effective at removing noise and recovering seismic signal.