Simultaneous source shooting or blended acquisition, which allows a temporal overlap between shot records, has been proposed as a method for substantially reducing the acquisition cost and improving data quality (e.g., denser shooting/efficient wide-azimuth shooting). Deblending allowed by traditional processing steps is still the dominant way of dealing with blended data. However, most of the deblending algorithms only utilize the blended input data without aliasing. In addition, it will be more challenging if the aliasing blended data traces are irregularly sampled, resulting in the failure of some deblending algorithms. In this paper we investigated the interpolation problem of irregularly blended seismic data using the focal transformation. Synthetic data example demonstrates the validity of its application for joint deblending and interpolation of the irregularly blended data.

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Blended/simultaneous source shooting is becoming more widely used in seismic exploration and monitoring, which can provide significant uplift in terms of both acquisition quality and economic efficiency. Effective deblending techniques are essential in order to make use of existing processing and imaging methodologies. When dealing with coarse and/or irregularly sampled blended data, the aliasing noises of incomplete data will affect the deblending process and the crosstalk in the blended data will also have a bad influence on the process of data reconstruction. In this work, we propose a joint deblending and data reconstruction method using the double focal transformation to eliminate blending noise and aliasing noise in the coarse, blended data. Synthetic and numerically blended field data examples demonstrate the validity of its application for deblending and data reconstruction.@en

In this work, we adopt a greedy inversion solver to design a fast version of the double focal transform that we can use to eliminate blending noise in simultaneous source acquisition. The greedy inversion introduces a coherence-oriented mechanism to enhance focusing of significant model space, leading to a sparse model space and fast convergence rate. Synthetics and numerically blended field data examples demonstrate the validity of its application for deblending. We also tested different inversion parameters (percentile value and weights) influencing the choice of the model subspace. The results indicate that by setting the percentile carefully and using weights it is possible to get better deblending results.@en