Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2016
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| _version_ | 1866912577042251776 |
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| author | Kumar, Rajiv López, Oscar Davis, Damek Aravkin, Aleksandr Y. Herrmann, Felix J. |
| author_facet | Kumar, Rajiv López, Oscar Davis, Damek Aravkin, Aleksandr Y. Herrmann, Felix J. |
| contents | Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for these techniques.
We propose a new approach to solve residual constrained formulations for interpolation. We represent the data volume using matrix factors, and build a block-coordinate algorithm with constrained convex subproblems that are solved with a primal-dual splitting scheme. The new approach is competitive with state of the art level-set algorithms that interchange the role of objectives with constraints. We use the new algorithm to successfully interpolate a large scale 5D seismic data volume, generated from the geologically complex synthetic 3D Compass velocity model, where 80% of the data has been removed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1607_02624 |
| institution | arXiv |
| publishDate | 2016 |
| record_format | arxiv |
| spellingShingle | Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach Kumar, Rajiv López, Oscar Davis, Damek Aravkin, Aleksandr Y. Herrmann, Felix J. Optimization and Control Machine Learning 62F35, 65K10 Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for these techniques. We propose a new approach to solve residual constrained formulations for interpolation. We represent the data volume using matrix factors, and build a block-coordinate algorithm with constrained convex subproblems that are solved with a primal-dual splitting scheme. The new approach is competitive with state of the art level-set algorithms that interchange the role of objectives with constraints. We use the new algorithm to successfully interpolate a large scale 5D seismic data volume, generated from the geologically complex synthetic 3D Compass velocity model, where 80% of the data has been removed. |
| title | Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach |
| topic | Optimization and Control Machine Learning 62F35, 65K10 |
| url | https://arxiv.org/abs/1607.02624 |