Single array-based three-dimensional ambient noise multi-mode surface wave imaging: A case study of Zhaoshu Island in the Xisha Islands

Array-based multi-modal surface wave imaging technology represents a significant breakthrough in the field of ambient noise surface wave imaging. By incorporating overtone surface wave dispersion curves, this method provides new independent constraints for subsurface structure inversion, significantly enhancing the detection capability of low-velocity structures. However, when applied to 3D velocity structure imaging, the array-based surface wave method requires dividing the study area into multiple sub-arrays, imposing high demands on the number and spatial distribution density of stations. In practical applications, due to limitations such as geographical constraints, economic costs, and time, station deployment often struggles to meet these requirements. To address this technical bottleneck, this study innovatively proposes a single-array station pair random sampling method. This approach extracts dispersion curves by randomly selecting different combinations of station pairs from a single array and inverts the phase velocity distributions at spatial grid points by the spatial phase-velocity further inversion (SPFI) technology. This approach can achieve 3D velocity structure imaging of multi-modal surface waves based on a single array comprising only a few dozen stations. To validate the effectiveness of the method, we applied it to Zhaoshu Island in the Xisha Islands—a typical island area where station deployment is challenging. The imaging results clearly reveal three low-velocity layers near the surface, at 100 meters, and around 250 meters depth, which are highly consistent with island drilling data and the characteristics of reef growth controlled by sea-level changes. The new method proposed in this study significantly reduces the implementation cost of multi-mode surface wave imaging, improves its applicability in complex environments, and provides important technical support for subsequent in-depth research.