Wave-equation dispersion inversion

Li, Jing; Feng, Zongcai; Schuster, Gerard T.

Wave-equation dispersion inversion

Files

ggw465.pdf (6.72 MB)

Type

Article

Authors

Li, Jing

Feng, Zongcai

Schuster, Gerard T.

KAUST Department

Center for Subsurface Imaging and Fluid Modeling
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division

KAUST Grant Number

OCRF-2014-CRG3-2300

Online Publication Date

2016-12-10

Print Publication Date

2017-03-01

Date

2016-12-10

Abstract

We present the theory for wave-equation inversion of dispersion curves, where the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. The dispersion curves are obtained from Rayleigh waves recorded by vertical-component geophones. Similar to wave-equation traveltime tomography, the complicated surface wave arrivals in traces are skeletonized as simpler data, namely the picked dispersion curves in the phase-velocity and frequency domains. Solutions to the elastic wave equation and an iterative optimization method are then used to invert these curves for 2-D or 3-D S-wave velocity models. This procedure, denoted as wave-equation dispersion inversion (WD), does not require the assumption of a layered model and is significantly less prone to the cycle-skipping problems of full waveform inversion. The synthetic and field data examples demonstrate that WD can approximately reconstruct the S-wave velocity distributions in laterally heterogeneous media if the dispersion curves can be identified and picked. The WD method is easily extended to anisotropic data and the inversion of dispersion curves associated with Love waves.

Citation

Li J, Feng Z, Schuster G (2016) Wave-equation dispersion inversion. Geophysical Journal International 208: 1567–1578. Available: http://dx.doi.org/10.1093/gji/ggw465.

Acknowledgements

We thank the financial support from the sponsors of the Consortium of Subsurface Imaging and Fluid Modeling (CSIM). We also thank KAUST for providing funding by the CRG grantOCRF-2014-CRG3-2300. For computer time, this research used the resources of the IT Research Computing Group and the Supercomputing Laboratory at KAUST. We thank them for providing the computational resources required for carrying out this work.