Hybrid rank-sparsity constraint model for simultaneous reconstruction and denoising of 3D seismic data
Hybrid rank-sparsity constraint model for simultaneous reconstruction and denoising of 3D seismic data
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DOI：
10.1190/geo2016-0557.1
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发表时间：
2017-08
期刊：
影响因子：
3.3
通讯作者：
Dong Zhang;Yatong Zhou;Hanming Chen;Wei Chen;S. Zu;Yangkang Chen
中科院分区：
文献类型：
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作者：
Dong Zhang;Yatong Zhou;Hanming Chen;Wei Chen;S. Zu;Yangkang Chen
本文提出了一种三维地震数据随机缺失道的同时重建和去噪方法。三维地震数据同时重建和去噪的核心是约束方法的选择。最近，已经有两种类型的流行的方法来选择这样的约束：稀疏促进变换使用稀疏约束和秩减少方法使用秩约束。虽然稀疏提升变换具有效率高的直接优点，但它缺乏对各种数据模式的适应性。另一方面，降秩方法可以自适应地应用于不同的数据集，但其计算成本相当高。我们研究了多个约束条件，同时地震数据重建和去噪的基础上一种新的混合秩稀疏约束（HRSC）模型，其目的是结合稀疏促进变换和降秩方法的好处。并设计了相应的HRSC算法。
ABSTRACTWe have determined an approach for simultaneous reconstruction and denoising of 3D seismic data with randomly missing traces. The core in simultaneous reconstruction and denoising of 3D seismic data is the choice of constraint method. Recently, there have been two types of popular approaches to choose such a constraint: sparsity-promoting transforms using a sparsity constraint and rank reduction methods using a rank constraint. Although the sparsity-promoting transform enjoys the direct advantage of high efficiency, it lacks adaptivity to a variety of data patterns. On the other hand, the rank reduction method can be adaptively applied to different data sets, but its computational cost is quite high. We investigate multiple constraints for simultaneous seismic data reconstruction and denoising based on a novel hybrid rank-sparsity constraint (HRSC) model, which aims at combining the benefits of the sparsity-promoting transforms and rank reduction methods. Also, we design the corresponding HRSC alg...