1887

Abstract

The regular and fine sampling along the time axis is common, whereas good spatial sampling is often more expensive or prohibitive and therefore is the main bottleneck for seismic resolution. We generalize missing data interpolation, a special case of data regularization, as a basis pursuit problem and use a Bregman iteration with seislet transform for recovering missing data. The seislet transform employs antialiasing dip pattern to handle aliasing information, which utilizes the scale-invariance property of prediction-error filters (PEFs). Bregman iteration provides the practical interpolation characteristics such as fast iteration convergence and reasonable interpolating result. Benchmark synthetic and field data tests confirm the effectiveness of the proposed iterative algorithm.

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/content/papers/10.3997/2214-4609.20148350
2012-06-04
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20148350
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