The time-lapse seismic method plays a critical role in reservoir monitoring and characterization. However, time-lapse data acquisitions are costly. Sparse acquisitions combined with postacquisition data reconstruction could reduce costs and facilitate more frequent applications of the time-lapse seismic monitoring. We have developed a sparse time-lapse seismic data reconstruction methodology based on compressive sensing. The method works with a hybrid of repeated and nonrepeated sample locations. To make use of the additional information from nonrepeated locations, we develop a view that nonrepeated samples in space are equivalent to irregular samples in calendar time. Therefore, we use these irregular samples in time coming from nonrepeated samples in space to improve the performance of compressive sensing reconstruction. The tests on synthetic and field data sets indicate that our method can achieve a sufficiently accurate reconstruction by using as few as 10% of the receivers or traces. The method not only works with spatially irregular sampling for dealing with the land accessibility problem and for reducing the number of nodal sensors, but it also uses the nonrepeated measurements to improve reconstruction accuracy.

中文翻译：

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使用压缩传感的时移地震数据重建

时移地震方法在储层监测和表征中起着至关重要的作用。然而，延时数据采集成本高昂。稀疏采集与采集后数据重建相结合可以降低成本并促进延时地震监测的更频繁应用。我们开发了一种基于压缩感知的稀疏延时地震数据重建方法。该方法适用于重复和非重复样本位置的混合。为了利用来自非重复位置的附加信息，我们提出了一种观点，即空间中的非重复样本等效于日历时间中的不规则样本。因此，我们及时使用这些来自空间中非重复样本的不规则样本来提高压缩感知重建的性能。对合成和现场数据集的测试表明，我们的方法可以通过使用少至 10% 的接收器或迹线来实现足够准确的重建。该方法不仅适用于空间不规则采样以处理土地可达性问题和减少节点传感器的数量，而且还使用非重复测量来提高重建精度。