Bayesian wavefield separation by transform-domain sparsity promotion |
Figure1-a,Figure1-b,Figure1-c,Figure1-d,Figure1-e,Figure1-f
Figure 1. Primary-multiple separation on a synthetic data volume. (a) The total data, . (b) Reference surface-related multiple-free data modeled with an absorbing boundary condition. (c) SRME-predicted primaries, . (d) Estimate for the primaries, using 3-D curvelets and single thresholding. (e) The same but with Bayesian thresholding for , and . (f) The same as (e) but now for . Notice the improvement in the estimated primaries by controlling the estimated multiples. By multiplying the and the other control parameters by a large factor, we diminished the control over the multiple prediction while keeping the sparsity penalties the same. Less control clearly adversely affects the estimated primaries, which is confirmed by the SNRs computed with respect to (b), i.e, versus . |
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Figure2-a,Figure2-b,Figure2-c,Figure2-d
Figure 2. Field data example of curvelet-domain primary-multiple separation. (a) Near-offset ( ) section for the total data plotted with automatic-gain control. (b) Estimate for the multiples, yielded by optimized multi-window SRME. (c) Corresponding estimate for the primaries using SRME. (d) Estimate for the primaries computed by Bayesian iterative thresholding with , and . Notice the improvement of the Bayesian curvelet-domain separation compared to SRME. Not only are the shallow multiples better removed but we also observe an improved continuity for the late primary arrivals. |
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Bayesian wavefield separation by transform-domain sparsity promotion |