3D interpolation of irregular data with a POCS algorithm
3D interpolation of irregular data with a POCS algorithm
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DOI：
10.1190/1.2356088
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发表时间：
2006-11-01
期刊：
影响因子：
3.3
通讯作者：
Kabir, Nurul
中科院分区：
文献类型：
--
作者：
Abma, Ray;Kabir, Nurul
地震调查通常有无法获得数据的不规则区域。这些数据应该经常进行插值。使用傅里叶变换的凸集投影（POCS）算法允许用简单的迭代方法对不规则填充的地震数据网格进行插值，从而产生高质量的结果。原始的2D图像恢复方法Gerchberg-Saxton算法很容易扩展到更高的维度，并且这里使用的3D版本的过程比典型的2D方法产生更好的插值。唯一对结果产生重大影响的参数是所使用的迭代次数，这个次数可以被高估，而不会降低结果的质量。这种简单性是一个显著的优点，因为它使用户不必进行大量的参数测试。虽然该算法的代价是典型二维方法的几倍，但该方法易于并行化，仍然完全实用。
Seismic surveys generally have irregular areas where data cannot be acquired. These data should often be interpolated. A projection onto convex sets (POCS) algorithm using Fourier transforms allows interpolation of irregularly populated grids of seismic data with a simple iterative method that produces high-quality results. The original 2D image restoration method, the Gerchberg-Saxton algorithm, is extended easily to higher dimensions, and the 3D version of the process used here produces much better interpolations than typical 2D methods. The only parameter that makes a substantial difference in the results is the number of iterations used, and this number can be overestimated without degrading the quality of the results. This simplicity is a significant advantage because it relieves the user of extensive parameter testing. Although the cost of the algorithm is several times the cost of typical 2D methods, the method is easily parallelized and still completely practical.