Joint Seismic Data Denoising and Interpolation with Double-Sparsity Dictionary Learning
Joint Seismic Data Denoising and Interpolation with Double-Sparsity Dictionary Learning
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DOI：
10.1088/1742-2140/aa6491
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Lingchen Zhu;E. Liu;J. McClellan
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文献类型：
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作者：
Lingchen Zhu;E. Liu;J. McClellan
地震数据质量对地球物理应用至关重要，因此数据恢复方法，包括去噪和插值，是地震数据处理流程中常见的初始步骤。提出了一种基于双稀疏字典学习的插值去噪方法。这扩展了以前仅用于去噪的工作。对原有的双稀疏字典学习算法进行了改进，通过定义一个掩蔽算子将其集成到字典的稀疏表示中来跟踪缺失数据的轨迹。采用加权低秩近似算法将字典更新作为一个受屏蔽算子约束的稀疏恢复优化问题来处理。与传统的固定字典稀疏变换缺乏适应复杂数据结构的能力相比，双稀疏字典学习方法在保持紧凑的正、逆变换算子的同时，从损坏地震数据的选定块中自适应地学习信号。在合成地震数据上的数值实验表明，与其他定向多尺度变换方法（如曲线变换）相比，该方法在不引入伪gibbs伪影的情况下保留了数据集中更多的细微特征。
Seismic data quality is vital to geophysical applications, so methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the dataset without introducing pseudo-Gibbs artifacts when compared to other directional multiscale transform methods such as curvelets.