论文标题
在迭代Marchenko方案中纠正不完美采样数据
Correcting for imperfectly sampled data in the iterative Marchenko scheme
论文作者
论文摘要
Marchenko方法检索了地下中虚拟来源的响应，这是所有倍数的所有顺序。该方法基于两个用于聚焦和格林功能的积分表示。这些积分以离散形式以对采集几何形状的有限总和表示。因此，该方法需要定期采样和共同定位的来源和接收器的理想几何形状。但是，最近的一项研究表明，从理论上讲，可以通过将不规则采样的结果与某些点扩散函数（PSF）进行解采样（PSF）来放松。然后将结果重建好像是使用完美的几何形状获得的。在这里，为了包括这些PSF提供了迭代的Marchenko计划。因此，显示在实际情况下如何考虑不完美的抽样。接下来，在2D数值示例上测试了新方法。结果显示了所提出的方案与标准迭代方案之间的明显改善。通过删除对完美几何形状的要求，Marchenko方法可以更广泛地应用于现场数据。
The Marchenko method retrieves the responses to virtual sources in the subsurface, accounting for all orders of multiples. The method is based on two integral representations for focusing and Green's functions. In discretized form these integrals are represented by finite summations over the acquisition geometry. Consequently, the method requires ideal geometries of regularly sampled and co-located sources and receivers. However, a recent study showed that this restriction can, in theory, be relaxed by deconvolving the irregularly-sampled results with certain point spread functions (PSFs).The results are then reconstructed as if they were acquired using a perfect geometry. Here, the iterative Marchenko scheme is adapted in order to include these PSFs; thus, showing how imperfect sampling can be accounted for in practical situations. Next, the new methodology is tested on a 2D numerical example. The results show clear improvement between the proposed scheme and the standard iterative scheme. By removing the requirement for perfect geometries the Marchenko method can be more widely applied to field data.
