Abstract
Simultaneous source acquisition can enhance the seismic data quality or improve the field acquisition efficiency. However, one of the disadvantages is that the simultaneous source data are often obtained on a non-uniform sampled grid in realistic acquisition. Except for the blending noise introduced by the temporal overlap, the non-uniform samplings usually cause serious artifacts which also greatly reduce the data quality and imaging resolution, such that data regularization must be implemented during the deblending. At present, most deblending algorithms are suitable for the uniformly sampled data. Thus, we propose the nonequispaced fast discrete curvelet transform-based deblending approach to deal with the non-uniformly sampled simultaneous source data. In order to enhance the deblending efficiency, we introduce the iterative shrinkage thresholding algorithm to solve the undetermined problem via a soft thresholding operator. Once the coefficients in the curvelet domain are acquired, uniformly sampled deblended data can be obtained via inverse curvelet transform. During the iterations, the threshold is decreased from a large value to a small value according to the new exponential function. Compared with the existing deblending methods that are applied on uniform grids, the key contribution is that the proposed method can directly work on the non-uniformly sampled simultaneous source data. The numerical examples are used to demonstrate the successful performance of the proposed method in attenuating blending noise and correcting the distorted events.
Highlights
Simultaneous source acquisition, or blended acquisition, which can save the acquisition cost and increase the data quality, has attracted significant attention from both academia and industry [1]–[3]
We propose a novel method for off-thegrid simultaneous source data using nonequispaced fast discrete curvelet transform (NFDCT) via iterative shrinkage thresholding algorithm (ISTA), which can remove blending noise and preserve useful signal effectively while projecting data from off-the-grid to the uniform grid
In order to improve the efficiency, we propose a new exponential thresholding formula decreased from a large value to a small value instead of the conventional thresholding formula, and applying a soft thresholding operator in the curvelet domain to provide a coherency-based constraint for the iterated model
Summary
Simultaneous source acquisition, or blended acquisition, which can save the acquisition cost and increase the data quality, has attracted significant attention from both academia and industry [1]–[3]. Filtering-based deblending method is efficient and easy to implement It cannot achieve a good performance when the blended data is very complex. Because of the ill-posed property of the deblending problem, inversion-based methods require some constraints, such as sparsity constraint and compressible constraint [24]–[27], and a regularization term often need to be introduced to ensure the uniqueness and stability of solution [28]. These two categories of deblending methods play
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
