Random noise attenuation by an amplitude-preserved time-frequency peak-based on empirical wavelet transform predictive filtreing

Time-frequency peak filtering (TFPF) is a classical filtering method in time-frequency domain. There is a pair of contradiction in this method, which effects the TFPF results. In order to improve the effectiveness of TFPF, we adopt empirical wavelet transform (EWT). It is the first time that EWT is applied in analyzing multichannel seismic data for the purpose of denoising. We know that adaptive representation of signal is very important in signal processing. EWT is a new adaptive signal decomposition technique, which is similar to the empirical mode decomposition but has a fully mathematical theory. The idea is to utilize the decomposition characteristic of EWT which decomposes a signal to several modes from low to high frequencyand to take advantage of the time-frequency filtering characteristic of TFPF which can recognize the valid signal component in the time-frequency plane in order to achieve effective random noise reduction together with good amplitude preservation. In the end, we show some experiment on synthetic seismic models and real data, we can see the effectiveness of this idea.