| 石敏*,朱震东* **,路昊* **,朱登明**,周军***.基于时空约束压缩感知的地震数据重建[J].高技术通讯(中文),2021,31(9):925~933 |
| 基于时空约束压缩感知的地震数据重建 |
| Seismic data reconstruction based on space-time constraint compressed sensing |
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| DOI：10.3772/j.issn.1002-0470.2021.09.003 |
| 中文关键词: 地震数据重建； 时空相关性； 压缩感知； 字典学习 |
| 英文关键词: seismic data reconstruction, space-time correlation, compressed sensing, dictionary learning |
| 基金项目: |
| 作者 | 单位 | | 石敏* | | | 朱震东* ** | | | 路昊* ** | | | 朱登明** | | | 周军*** | |
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| 中文摘要: |
| 在实际勘探中，由于环境、设备或人为因素的影响, 采集的地震数据中有很多丢失的数据，严重影响了数据的解释工作。针对这一问题，根据地震数据的时空相关性，提出了一种基于时空约束压缩感知的地震数据重建方法。该方法使用内核奇异值分解(K-SVD)字典学习算法训练超完备字典作为稀疏变换基，进而利用改进的稀疏自适应匹配追踪算法(SAMP)完成重建。通过初始稀疏性估计和变步长策略，减少了SAMP中收敛所需的迭代次数。利用真实的地震数据和微电阻率成像数据进行实验，将所提出的方法与压缩感知重建算法进行了比较，不仅提高了重建数据的准确性，而且缩短了执行时间。 |
| 英文摘要: |
| In actual exploration, due to the influence of environment, equipment or human factors, there are a lot of missing data in the seismic data collected, which seriously affects the data interpretation work. Aiming at this problem, according to the space-time correlation of seismic data, a method of seismic data reconstruction based on space-time constrained compressed sensing is proposed. In this method, an over-complete dictionary as a sparse transform basis is trained using kernel singular value decomposition (K-SVD) dictionary learning algorithm. The reconstruction is accomplished using an improved sparsity of adaptive matching pursuit (SAMP). By incorporating an initial sparsity estimation step and adopting a variable step size strategy, the number of iterations needed for convergence in SAMP can be significantly reduced. Using real seismic data and micro-resistivity imaging data, the proposed novel method is compared with state-of-the-art compressive sensing reconstruction algorithms. The experimental results show that the accuracy of the reconstructed data is significantly improved, and the execution time is also reduced. |
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