Shaohuan Zu

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The seislet transform can be used to interpolate regularly undersampled seismic data if an accurate local slope map can be obtained. The dealiasing capability of such method highly depends on the accuracy of the estimated local slope, which can be achieved by using the low-frequency components of the aliased seismic data in an iterative manner. Previous(More)
Seismic data interpolation or reconstruction plays an important role in seismic data processing. Many processing steps, such as high resolution processing, wave-equation migration, amplitude-versus-offset and amplitude-versus-azimuth analysis, require regularly sampled data. The reconstruction can be posed as an inverse problem, which is known to be ill(More)
We propose a novel approach for removing noise from multiple reflections based on an adaptive randomized-order empirical mode decomposition (EMD) framework. We first flatten the primary reflections in common midpoint gather using the automatically picked normal moveout velocities that correspond to the primary reflections and then randomly permutate all the(More)
The low-rank approximation method is one of the most effective approaches recently proposed for attenuating random noise in seismic data. However, the low-rank approximation approach assumes that the seismic data has low rank for its <inline-formula> <tex-math notation="LaTeX">$f-x$ </tex-math></inline-formula> domain Hankel matrix. This assumption is(More)
Observed seismic data are mostly irregularly sampled and seismic data interpolation is an essential procedure to provide accurate complete data for seismic data analysis, such as amplitude-versus-offset analysis, multiple suppression, and wave-equation migration. The well-known minimum weighted norm interpolation (MWNI) method could achieve a relatively(More)
The proximal splitting algorithm, which reduces complex convex optimization problems into a series of smaller subproblems and spreads the projection operator onto a convex set into the proximity operator of a convex function, has recently been introduced in the area of signal processing. Following the splitting framework, we propose a novel three-operator(More)
Optimal stacking of multiple data sets plays a significant role in many scientific domains. The quality of stacking will affect the signal-to-noise ratio and amplitude fidelity of the stacked image. In seismic data processing, the similarity-weighted stacking makes use of the local similarity between each trace and a reference trace as the weight to stack(More)
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