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Using Multiple Voronoi Partitions to Conduct Array-Based Ambient Noise Surface Wave Imaging

Description: 

With the advancement of array-based surface wave technology, a breakthrough has been achieved in obtaining high-mode surface wave dispersion curves from ambient noise cross-correlation to detect subsurface structures. Compared to using only fundamental surface wave dispersion curves, including high-mode surface wave dispersion curves in multi-modal surface wave inversion can effectively enhance constraints on subsurface structures, especially low-velocity structures. However, the study area needs to be divided into multiple subarrays when conducting three-dimensional velocity structure imaging due to the necessity of array data for each dispersion extraction. The size and shape of these subarrays may impact the effectiveness of the final dispersion extraction.

In this work, we propose a method based on randomly generating Voronoi polygons for repeated subdivision subarrays to circumvent the need to select array size and shape. This method primarily involves iteratively generating random Voronoi polygons to create different partitions. These dispersion curves extracted from one partition are treated as samples of all points within the corresponding Voronoi polygon. After sufficient iterations, statistical methods can be employed to obtain dispersion curves for various points in the study area and estimate the associated errors. Furthermore, this method can address the issue of varying array scales required to extract different modes' dispersion curves. We will illustrate the specific methodology using the imaging of Lasso dense arrays as an example, hoping that this method will effectively enhance the applicability of array surface wave techniques.

Session: Earth’s Structure from the Crust to the Core [Poster Session]

Type: Poster

Date: 5/2/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Zhengbo

Student Presenter: No

Invited Presentation: 

Authors