Estimating S-wave velocities from 3D 9-component shallow seismic data using local Rayleigh-wave dispersion curves – A field study

Abstract

Surface waves are widely used to delineate near-surface structures. Most of the surface-wave studies focus on 2D and single-component data, how to use the surface-wave method on 3D multi-component data has not been studied. In this paper, we perform a 3D 9-component (9C) shallow-seismic survey in Rheinstetten, Germany. We transform the 9C data from the cartesian coordinates to the ray-based coordinates and separate Love and Rayleigh waves in the transformed data. The data is windowed into groups constituted by 16 receiver points, and we perform dispersion analysis in each windowed data individually. Rayleigh-wave energy acquired by four different components in each windowed data is stacked in the frequency-velocity domain to improve the accuracy of the dispersion image, and the Rayleigh-wave dispersion curve is picked along the energy peak in the stacked dispersion image. We estimate a total of 242 Rayleigh-wave dispersion curves from the 3D 9C data, which are then inverted to build a 3D S-wave velocity model. The 3D S-wave velocity model shows the existence as well as the spatial variation of the main geological feature, namely a refilled trench from the 18th century. We compare 2D sections of the 3D model to the GPR profiles acquired in the same survey area, which proves the relatively high accuracy of the structures in the estimated model. We also compare the observed seismic waveforms to the synthetic waveforms simulated by using the 3D model, which also proves that the estimated model is fairly reliable. This study provides a cost-efficient way to use surface waves in 3D multi-component data to image the near-surface S-wave velocity model.