To get the best result for seismic imaging using primary reflections, data with densely-spaced sources and receivers are ideally preferred. However, dense acquisition can sometimes be hindered by various obstacles, like platforms or complex topography. Such areas with large data gaps may deter exploration or monitoring, as conventional imaging strategies would either provide poor seismic images or turn out to be very expensive. Surface-related multiples travel along different paths compared to primaries, illuminating a wider subsurface area and hence making them valuable in case of data with large gaps. We propose different strategies of using surface-related multiples to get around the problem of imaging in the case of a large data gap. Conventional least-squares imaging methods that incorporate surface-related multiples do so by re-injecting the measured wavefield in the forward-modelling process, which makes it still sensitive to missing data. We introduce a ‘non-linear’ inversion approach in which the surface multiples are modelled from the original source field. This makes the method less dependent on the receiver geometry, therefore, effectively exploiting the information from surface multiples in cases of limited illumination. However, such an approach is sensitive to the knowledge of the source properties. Therefore, we propose a ‘hybrid’ method that combines the non-linear imaging method with the conventional ‘linear’ multiple imaging method, which further improves our imaging result. We test the methods on numerical as well as field data. The results indicate substantial removal of artefacts in the image derived from linear imaging methods due to incomplete data, by exploiting the surface multiples to a maximum extent.

中文翻译：

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使用与表面相关的倍数成像以克服较大的采集间隙

为了获得使用一次反射进行地震成像的最佳结果，理想地首选具有密集空间的源和接收器的数据。但是，有时会因各种障碍（例如平台或复杂的地形）而阻碍密集采集。此类数据缺口较大的区域可能会阻止勘探或监视，因为常规成像策略可能会提供较差的地震图像，或者会变得非常昂贵。与原始图像相比，与表面相关的倍数沿着不同的路径传播，从而照亮了更宽的次表面区域，因此使它们在具有较大间隙的数据中很有价值。我们提出了不同的使用表面相关倍数的策略来解决在数据缺口较大的情况下成像的问题。结合表面相关倍数的常规最小二乘法成像方法是通过在正向建模过程中重新注入测量的波场来实现的，这仍然使它对丢失的数据敏感。我们引入了一种“非线性”反演方法，其中从原始源场模拟表面倍数。这使得该方法较少依赖于接收器的几何形状，因此，在光照受限的情况下，可以有效地利用来自表面倍数的信息。但是，这种方法对源属性的知识很敏感。因此，我们提出了一种将非线性成像方法与常规“线性”多重成像方法相结合的“混合”方法，从而进一步改善了成像效果。我们在数值和现场数据上测试这些方法。