Learning the blending spikes using sparse dictionaries
Learning the blending spikes using sparse dictionaries
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DOI：
10.1093/gji/ggz200
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发表时间：
2019-05
影响因子：
2.8
通讯作者：
Yangkang Zhang;S. Zu;Wei Chen-;Mi Zhang;Zhe Guan
中科院分区：
文献类型：
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作者：
Yangkang Zhang;S. Zu;Wei Chen-;Mi Zhang;Zhe Guan
在现代混合同震源地震采集中，脱混对制备高质量地震数据起着重要作用。最先进的去混是基于稀疏约束迭代反演的。基于反演的去混假设环境噪声水平较低，迭代反演过程中的数据失拟是随机环境噪声的结果。当随机环境噪声变得非常强，反演迭代拟合随机噪声而不是信号和混合干扰时，传统方法就会出现问题。提出了一种考虑强随机噪声的约束反演模型，即使在强随机噪声存在的情况下也能获得满意的反演结果。该方法的原理是使用稀疏字典学习混合尖峰，从而学习到的字典原子能够区分混合尖峰和随机噪声。通过迭代反演框架可以更好地拟合分离信号和混合尖峰。通过综合和现场数据实例验证了该方法的有效性。
Deblending plays an important role in preparing high-quality seismic data from modern blended simultaneous-source seismic acquisition. State-of-the-art deblending is based on the sparsity-constrained iterative inversion. Inversion-based deblending assumes that the ambient noise level is low and the data misfit during iterative inversion accounts for the random ambient noise. The traditional method becomes problematic when the random ambient noise becomes extremely strong and the inversion iteratively fits the random noise instead of the signal and blending interference. We propose a constrained inversion model that takes the strong random noise into consideration and can achieve satisfactory result even when strong random noise exists. The principle of this new method is that we use sparse dictionaries to learn the blending spikes and thus the learned dictionary atoms are able to distinguish between blending spikes and random noise. The separated signal and blending spikes can then be better fitted by the iterative inversion framework. Synthetic and field data examples are used to demonstrate the performance of the new approach.