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Missing trace reconstruction for 2D land seismic data with randomized sparse sampling
Geophysics ( IF 3.3 ) Pub Date : 2021-05-13 , DOI: 10.1190/geo2020-0683.1
Iga Pawelec 1 , Michael Wakin 2 , Paul Sava 1
Affiliation  

Acquisition of high-quality land seismic data requires (expensive) dense source and receiver geometries to avoid aliasing-related problems. Alternatively, acquisition using the concept of compressive sensing (CS) allows for similarly high-quality land seismic data using fewer measurements provided that the designed geometry and sparse recovery strategy are well matched. We have developed a complex wavelet-based sparsity-promoting wavefield reconstruction strategy to overcome challenges in land seismic data interpolation using the CS framework. Despite having lower angular sensitivity than curvelets, complex wavelets improve the reconstruction of sparsely acquired land data while being faster and requiring less storage. Unlike the Fourier transform, the complex wavelet transform localizes aliasing-related artifacts likely to be present in field data and yields reconstructions with fewer artifacts and higher signal-to-noise ratios. We determine that the data recovery success depends on the number and the geometry of the missing traces as revealed by analyzing reconstructions from multiple realizations of trace geometry and data decimation ratios. Using half the number of traces required by the regular sampling rules and thus reducing the acquisition costs, we find that data are appropriately reconstructed provided that there are no large gaps in the strategic places.

中文翻译:

随机稀疏采样的二维陆地地震数据丢失迹重建

采集高质量的陆地地震数据需要(昂贵的)密集的源和接收器几何形状,以避免与混叠相关的问题。或者,使用压缩感测(CS)概念进行采集,只要设计的几何形状和稀疏恢复策略能够很好地匹配,就可以使用较少的测量结果获得类似的高质量陆地地震数据。我们已经开发了一种基于小波的复杂稀疏促进波场重建策略,以克服使用CS框架进行陆地地震数据插值的挑战。尽管复杂度小波的角度敏感度比曲波小,但复杂的小波却改善了稀疏采集的陆地数据的重建,同时速度更快且所需存​​储量更少。与傅立叶变换不同,复杂的小波变换将可能出现在现场数据中的与混叠相关的伪影本地化,并产生具有更少伪影和更高信噪比的重构。我们确定了数据恢复的成功取决于丢失迹线的数量和几何形状,这是通过分析迹线几何形状和数据抽取率的多种实现的重构来揭示的。使用常规采样规则所需的痕迹数量的一半,从而降低了购置成本,我们发现,只要在战略位置没有较大的差距,就可以适当地重建数据。我们确定了数据恢复的成功取决于丢失迹线的数量和几何形状,这是通过分析迹线几何形状和数据抽取率的多种实现的重构来揭示的。使用常规采样规则所需的一半迹线数量,从而降低了采购成本,我们发现,只要在战略位置没有较大的差距,就可以适当地重建数据。我们确定了数据恢复的成功取决于丢失迹线的数量和几何形状,这是通过分析迹线几何形状和数据抽取率的多种实现的重构来揭示的。使用常规采样规则所需的痕迹数量的一半,从而降低了购置成本,我们发现,只要在战略位置没有较大的差距,就可以适当地重建数据。
更新日期:2021-05-18
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