Retrieving lateral variations from surface wave dispersion curves^‡

Abstract

A B S T R A C TSurface wave analysis is usually applied as a 1D tool to estimate V S profiles. Here we evaluate the potential of surface wave analysis for the case of lateral variations. Lateral variations can be characterized by exploiting the data redundancy of the ground roll contained in multifold seismic data. First, an automatic processing procedure is applied that allows stacking dispersion curves obtained from different records and which retrieves experimental uncertainties. This is carried out by sliding a window along a seismic line to obtain an ensemble of dispersion curves associated to a series of spatial coordinates. Then, a laterally constrained inversion algorithm is adopted to handle 2D effects, although a 1D model has been assumed for the forward problem solution. We have conducted different tests on three synthetic data sets to evaluate the effects of the processing parameters and of the constraints on the inversion results. The same procedure, applied to the synthetic data, was then tested on a field case. Both the synthetic and field data show that the proposed approach allows smooth lateral variations to be properly retrieved and that the introduction of lateral constraints improves the final result compared to individual inversions.

Time-lapse monitoring of fluid-induced geophysical property variations within an unstable earthwork using P-wave refraction

“…To choose the optimal level of spatial smoothing, a series of trial inversions of the reference data set (March 2014) were carried out, with different degrees of lateral smoothing. By comparing the distributions of normalized residuals from the various inversions ( Figure 5), a medium level of spatial regularization was identified as optimal, leading to realistically homogeneous V P sections without significantly penalizing the data fitting (Boiero, 2009;Boiero and Socco, 2010). Further increases in the degree of spatial smoothing caused an appreciable increase of misfit ( Figure 5).…”

Time-lapse monitoring of climate effects on earthworks using surface waves

“…In the adopted inversion procedure, a single initial model was estimated and subsequently used for the deterministic inversion of all dispersion curve data sets (July 2013 to November 2014), following the approach introduced by Socco et al (2009) and Boiero and Socco (2010).…”

“…In the LCI inversion of the SW dispersion curves, the selection of optimal levels of spatial regularization was of considerable importance for reconciling data compliance and spatial variability and ensuring more physically consistent seismic models (Boiero, 2009;Boiero and Socco, 2010). The selection of the level of spatial regularization was based on a set of trial inversions performed on the March 2014 reference data set, adopting an increasing strength for spatial constraints, and collating the obtained levels of data fitting (measured as normalized residuals for each experimental dispersion curve, Figure 6).…”

“…The common initial model for the parallel inversion of all dispersion curve data sets was obtained from Monte Carlo (MC) inversion of the sample dispersion curves from the March 2014 data set Boiero and Socco, 2010;Konstantaki et al, 2013). March 2014 was selected as the reference data set for its central position with respect to the surveyed period of time.…”

Surface wave attenuation in the shallow subsurface from multichannel–multishot seismic data: a new approach for detecting fractures and lithological discontinuities

“…Multichannel analysis of surface waves (MASW; Park et al 1998Park et al , 1999 is currently the most effective method to estimate dispersion curves from multichannel seismic data. To overcome the assumption of one-dimensional velocity structures in surface wave analysis, several workers have proposed methods to estimate quasi-two-dimensional dispersion curves with high spatial resolution (e.g., Hayashi and Suzuki 2004;Boiero and Socco 2010;Bergamo et al 2012;Ikeda et al 2013).…”