Abstract

Surface wave tomography is a feasible method to provide complementary data to better understand the continuity of soil strata. It may also be used to determine best locations for invasive testing. Surface wave tomography methods are frequently used to delineate soil-rock interface and determine average shear wave velocity for seismic site class determination. Delineation within the soils layers where the change in seismic velocity within the soil layers are relatively small requires higher resolution data collection and enhanced processing methods than those used historically in surface wave tomography. This study uses a patented imaging and monitoring system based on the passive seismic interferometry technique augmented by active seismic sources at known locations. A dense nodal array of MEMs accelerometers were deployed at a site where known saturated loose materials exist. This paper presents shear wave velocity (Vs) data deduced from surface wave measurements where various, compression wave velocity (Vp) to Vs ratios were considered, especially showing the effect of Vp/Vs ratio, on the Vs data. This ratio which was traditionally assumed can have a significant effect on the inferred Vs. This paper also presents the comparison of deduced Vs data to measured Vs data by seismic cone penetration test and makes some recommendations for how to improve the methods to determine shear wave velocity in soft/loose soils.

Abstract

The surface wave method (SWM) and the screw weight sounding (SWS) are employed as a geophysical exploration method and a sounding test, respectively to identify the spatial distribution of the stiffness of an earth-fill dam in the present study. The ensemble Kalman filter (EnKF) is used as a data assimilation technique. It can estimate the spatial distribution of the Young’s modulus as the stiffness of an earth-fill dam by assimilating the travel time to the first arrival of the surface waves. By the ensemble data assimilation, the measured data from the SWM is applied to simultaneously estimate the Young's modulus and evaluate the uncertainties. The SWS results are employed as the prior information to generate the initial ensemble through the sequential Gaussian simulation (sGs). Proposed method has been applied to the actual data of the SWM and the SWS measured at an earth-fill dam site. Consequently, it has been clarified the proposed approach could identify the appropriate random field of Young's modulus.