Iterative Deblending Based on 2-D Fast Fourier Transform With Multistage Median Bilateral Filtering

Simultaneous source technology can enhance the efficiency of seismic data acquisition, however, blending noise significantly hinders subsequent seismic data processing. To deal with blending noise, an improved iterative deblending algorithm is proposed to separate simultaneous source data. Considering the spatial incoherence between useful signal and blending noise in certain sorting domains, including common receiver domain and common offset domain, blended data was transformed from the time-space (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i>) domain to the frequency-wavenumber (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>) domain to extract useful signal. However, the sparse representation capability of two-dimensional (2D) fast Fourier transform (FFT) is limited, particularly, for the case that weak coherent signal is contaminated by strong incoherent noise. To relieve this issue, a filter named multistage median bilateral filtering (BFMLM) was integrated into the iterative deblending framework to enhance the sparse representation of 2D FFT by attenuating blending noise. This improvement can maximize the extraction of useful signal and significantly reduce noise leakage, thereby achieving better deblended data. Two sets of synthetic data and two sets of field data were applied to evaluate the deblending performance of the methods based on 2D FFT, curvelet transform (CT) and the proposed method. With BFMLM, the deblending quality of the proposed method is better than that of FFT method. Comparing the deblending method by CT, the proposed method can obtain the similar deblending performance with less computational cost.