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Most of the nonstationary PFs/PEFs use iterative or recursive approaches to calculate their coefficients. This leads to high computational costs, especially in the storage of variable coefficients (Ruan et al., 2015). Recently, a streaming PEF (Fomel and Claerbout, 2016) was proposed to solve this problem. This method updates the PEF coefficients incrementally as new data arrive. This method reduces the computational cost of the streaming PEF to a single convolution. Moreover, the exact inversion of the streaming PEF makes missing data interpolation straightforward.
In this paper, we propose an adaptive PF method based on streaming and orthogonalization (Chen and Fomel, 2015) to attenuate random noise in nonstationary seismic data. The proposed method is able to characterize the nonstationarity on both time and space axes. The streaming element makes the proposed method a convenient and fast denoising approach. The application of orthogonalization further strengthens its ability in random noise attenuation. Numerical tests using synthetic and field data demonstrate the effectiveness of the proposed SOPF method.
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