Spatiotemporal Adaptive Time–Frequency Peak Filtering for Seismic Random Noise Attenuation

Recently, the time-frequency peak filtering (TFPF) has been frequently applied to seismic random noise attenuation. Conventionally, TFPF recovers seismic events in association with a fixed window length along the time direction. Thus, the spatial information of the events is ignored. Moreover, TFPF is approximately equivalent to a time-invariant low-pass filter, which cannot adapt quickly enough to track the rapidly changing signal. To solve these problems, we propose a spatiotemporal adaptive TFPF (ST-ATFPF) algorithm. In this approach, we recover the events by the ATFPF, which is based on convex sets and Viterbi algorithm in intercept time-slowness domain. The high-resolution Radon transform concentrates the energy of the events effectively, and the correlations between wavelets, which form the events in a seismic record, are exploited by ST-ATFPF, consequently. Additionally, ST-ATFPF can track the rapidly changing signal. Numerical results have demonstrated the validity of our algorithm with higher output SNR and less signal information loss than the traditional TFPF, Radon TFPF, and ATFPF.