The elastic angle gathers of multicomponent sparse OBN data by Gaussian-beam migration

Abstract Multicomponent ocean-bottom node (OBN) data, which includes both pressure and multicomponent displacement measurements, can offer significant insights into the elastic properties of the subsurface in deep-water areas. To enhance imaging quality and better understand these insights, angle gathers are potentially beneficial. However, many OBN surveys involve roughly sampled receiver locations, resulting in sparse and irregular geometries. This presents a significant challenge when attempting to extract high-quality angle gathers due to issues such as low-fold and pronounced aliasing, especially for converted PS waves. To tackle this problem, we introduce an angle-domain common-receiver Gaussian-beam migration (GBM) method of multi-measurement data aimed at producing elastic angle gathers. The GBM is well-suited for this due to its automatic denoising, enabled by the local slant stacking typically used in GBM, and its angular interpolation capabilities from the local plane-wave nature of Gaussian beams. Moreover, performing migration in the common-receiver domain can handle sparse and irregular sampling better. By processing multi-measurement data simultaneously, we present the imaging condition with mode decomposition in the angle domain, incorporating deghosting processes on the receiver side. We applied our method to synthetic 4C OBN data sets and demonstrated its effectiveness in generating accurate elastic angle gathers. Artifact removal is well observed in angle gathers using our 4C approach. We also explored the effects of increasing the node spacing by up to 800 m. The results indicate that even with such sparse data, our method still has the ability to produce relatively high-quality angle gathers.