A data-dependent Fourier filter based on image segmentation for random seismic noise attenuation

In this paper, we propose a data-dependent Fourier filter (DDFF) based on image segmentation for random seismic noise attenuation. In the proposed method, the original seismic data is divided into some overlapped small blocks. For each block, a local Fourier filter is designed automatically in two steps. At first, a binary mask is obtained by segmenting the Fourier amplitude spectra (FAS) of this block. The histogram of the FAS is used to get the threshold for the FAS segmentation. Secondly, an average filter is applied on the binary mask to get the tapered Fourier filter. In the proposed method, the DDFFs for all blocks are compact and time-space variant. After all blocks are processed, they are merged together to form the filtered result. We illustrate our method by a 2D synthetic seismic data, and give a comparison with the coherent event extraction method in Fourier domain. At last, a real 3D seismic data example demonstrates that the proposed method obtains some promising results.