Constrained Surface-wave Dispersion Inversion Using GPR Reflection Data

2020 ◽  
Author(s):  
Shufan Hu ◽  
Yonghui Zhao ◽  
Wenda Bi ◽  
Ruiqing Shen ◽  
Bo Li ◽  
...  
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Reflection Data ◽  
Uniqueness Of The Solution ◽  
S Model

<p>Ground penetrating radar (GPR) and Seismic Surface Wave methods (SWMs) are two nondestructive testing (NDT) methods commonly used in near-surface site investigations. These two methods investigate the media properties of subsurface based on different physical phenomena. GPR has a good resolvability to characterize the layered structure since the propagation of electromagnetic wave is sensitive to the change of electrical properties, while, the geometric dispersion of surface waves can be used to retrieve the variation of S-wave velocity (<em>V</em>s) with depth. In most situations, these two data sets are processed separately, and the results are later used for comprehensive interpretation. Constrained inversion, as a way to implement data fusion, can alleviate the non-uniqueness of the solution and produce more consistent information for the comprehensive site and material investigations.</p><p>We present an algorithm for the inversion of surface-wave dispersion curves with GPR interface constraints in 2D media. The reflection interfaces interpreted from the GPR profile are integrated into a cell- and boundary-based <em>V</em>s model. This implementation allows both vertical and lateral changes within each region while also allows sharp changes across the boundaries. In addition, our algorithm simultaneously inverts several dispersion curves extracted along the survey line using multi-size spatial windows, which mitigates the adverse effects of 1D assumption in traditional surface-wave dispersion inversion and improves the matching of GPR and SWMs in lateral variations. We use synthetic and field data sets to test the effectivity of the proposed method. Both results show the improved resolution of the <em>V</em>s model retrieved by the constrained inversion compared to the standard inversion.</p>

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

Multi-Window Weighted Stacking of Surface-Wave Dispersion

Geophysics ◽  
2020 ◽  
pp. 1-53
Author(s):  
Sylvain Pasquet ◽  
Wei Wang ◽  
Po Chen ◽  
Brady A. Flinchum
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Real Data ◽  
Wave Dispersion ◽  
Lateral Resolution ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Shallow Subsurface ◽  
Depth Of Investigation

Surface wave (SW) methods are classically used to characterize shear (S-) wave velocities ( VS) of the shallow subsurface through the inversion of dispersion curves. When targeting 2D shallow structures with sharp lateral heterogeneity, windowing and stacking techniques can be implemented to provide a better description of VS lateral variations. These techniques, however, suffer from the trade-off between lateral resolution and depth of investigation, well-known when using multichannel analysis of surface waves (MASW). We propose a novel methodology aimed at enhancing both lateral resolution and depth of investigation of MASW results through the use of multi-window weighted stacking of surface waves (MW-WSSW). MW-WSSW consists in stacking dispersion images obtained from data segments of different sizes, with a wavelength-based weight that depends on the aperture of the data selection window. In that sense, MW-WSSW provides additional weight to short wavelengths in smaller windows so as to better inform shallow parts of the subsurface, and vice versa for deeper velocities. Using multiple windows improves the depth of investigation, while applying wavelength-based weights enhances shallow lateral resolution. MW-WSSW was implemented within the open-source package SWIP, and applied to the processing of synthetic and real data sets. In both cases we compared it with standard windowing and stacking procedures that are already implemented in SWIP. MW-WSSW provided convincing results with optimized lateral extent, improved shallow resolution, and increased depth of investigation.

Estimating surface-wave dispersion curves from 3D seismic acquisition schemes: Part 1 — 1D models

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. G85-G93 ◽  
Author(s):  
Daniele Boiero ◽  
Paolo Bergamo ◽  
Roberto Bruno Rege ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
P Wave ◽  
Seismic Exploration ◽  
3D Seismic ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Orthogonal Geometry ◽  
Seismic Acquisition

Surface-wave analysis is based on the estimation of surface-wave dispersion curves, which are then inverted to provide 1D S-wave velocity profiles. Surface-wave dispersion curves can be extracted from P-wave records obtained in seismic exploration and used to characterize the ground structure at a shallow depth. Dispersion curve estimation using 2D wavefield transforms is well-established for 2D acquisition schemes (in-line source and receiver spread). It is possible to extract surface-wave dispersion curves using 2D wavefield transforms from 3D seismic data acquired with any acquisition scheme. In particular, we focus on areal geometry and orthogonal geometry, and we provide a method based on the analysis in the offset domain and the [Formula: see text] multiple signal classification (MUSIC) transform. We assess the performance of the method on synthetic and field data concerning 1D sites.

Surface Wave Dispersion Imaging Using Improved τ-p Transform Approach

2013 ◽  
Vol 353-356 ◽  
pp. 1196-1202 ◽  
Author(s):  
Jian Qi Lu ◽  
Shan You Li ◽  
Wei Li
Keyword(s):  
Surface Wave ◽  
Phase Shift ◽  
Dispersion Curve ◽  
Wave Dispersion ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Narrow Frequency Range ◽  
Channel Analysis ◽  
Rayleigh Wave Dispersion

Surface wave dispersion imaging approach is crucial for multi-channel analysis of surface wave (MASW). Because the resolution of inversed S-wave velocity and thickness of a layer are directly subjected to the resolution of imaged dispersion curve. The τ-p transform approach is an efficient and commonly used approach for Rayleigh wave dispersion curve imaging. However, the conventional τ-p transform approach was severely affected by waves amplitude. So, the energy peaks of f-v spectrum were mainly gathered in a narrow frequency range. In order to remedy this shortage, an improved τ-p transform approach was proposed by this paper. Comparison has been made between phase shift and improved τ-p transform approaches using both synthetic and in situ tested data. Result shows that the dispersion image transformed from proposed approach is superior to that either from conventionally τ-p transform or from phase shift approaches.

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