When the depth of the shallow three‐dimensional seismic exploration is less than 100 m, one often encounters very low velocities for the target and high frequencies in the data. Following Nyquist–Shannon sampling theorem, the permissible maximum receiver interval can be smaller in this case compared to relatively deeper seismic exploration. This suggests that there are still issues to be addressed in the design of geometry and in data processing in shallow three‐dimensional seismic exploration. This paper addresses these problems by applying the theory of compressed sensing for signal processing to shallow‐seismic geometry designing and data processing. Theoretical research shows that random sampling of data can better reconstruct the wavefield than undersampled data. Random sampled data can transform the coherent aliasing to non‐coherent noise, which turns the seismic data interpretation problem into a data denoising problem. The jittered random sampling method avoids the situation when the spatial data points of a randomly sampled dataset are too concentrated or too sparse. Our proposed approach was tested on simulated and real seismic data. The results show that if the jittered random undersampling method is used in shallow three‐dimensional seismic data acquisition, then a wider range of observation with fewer receivers in the layout is possible. This greatly improves the data collection efficiency in the field. In addition, the random sampling method has more flexibility in the field environment. When using the regular sampling method, an open survey area without large obstacles is needed. However, the random sampling method can be adapted to rugged terrains. When obstacles are encountered, the receiver spacing can be increased appropriately. In open areas, the receiver spacing can be decreased to compensate for the reduced data.

中文翻译：

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在浅3D地震勘测中设计几何和成像时使用抖动采样

当浅层三维地震勘探的深度小于100 m时，通常会遇到极低的目标速度和数据中的高频。遵循Nyquist–Shannon采样定理，与相对较深的地震勘探相比，在这种情况下，允许的最大接收器间隔可以更小。这表明在浅层三维地震勘探中的几何设计和数据处理中仍然需要解决一些问题。本文通过将用于信号处理的压缩传感理论应用于浅地震波几何设计和数据处理，解决了这些问题。理论研究表明，与欠采样数据相比，随机采样数据可以更好地重建波场。随机采样的数据可以将相干混叠转换为非相干噪声，从而将地震数据解释问题转化为数据去噪问题。抖动随机采样方法避免了随机采样数据集的空间数据点过于集中或稀疏的情况。我们提出的方法已在模拟和真实地震数据上进行了测试。结果表明，如果在浅层三维地震数据采集中使用抖动随机欠采样方法，则可以在布局范围内以更少的接收器实现更大范围的观测。这大大提高了现场的数据收集效率。另外，随机采样方法在现场环境中具有更大的灵活性。使用常规采样方法时，需要没有大障碍的开放调查区域。但是，随机采样方法可以适应崎terrain的地形。当遇到障碍物时，可以适当增加接收器的间距。在空旷地区，可以减小接收机间隔以补偿减少的数据。