Ray-tracing traveltime tomography versus wave-equation traveltime inversion for near-surface seismic land data

Fu, Lei; Hanafy, Sherif M.

Ray-tracing traveltime tomography versus wave-equation traveltime inversion for near-surface seismic land data

Files

int-2016-0210.1.pdf (7.43 MB)

Type

Article

Authors

Fu, Lei
Hanafy, Sherif M.

KAUST Department

Cairo University, Geophysics Department, Thuwal, Saudi Arabia
Physical Science and Engineering (PSE) Division

Online Publication Date

2017-05-11

Print Publication Date

2017-08-31

Date

2017-05-11

Abstract

Full-waveform inversion of land seismic data tends to get stuck in a local minimum associated with the waveform misfit function. This problem can be partly mitigated by using an initial velocity model that is close to the true velocity model. This initial starting model can be obtained by inverting traveltimes with ray-tracing traveltime tomography (RT) or wave-equation traveltime (WT) inversion. We have found that WT can provide a more accurate tomogram than RT by inverting the first-arrival traveltimes, and empirical tests suggest that RT is more sensitive to the additive noise in the input data than WT. We present two examples of applying WT and RT to land seismic data acquired in western Saudi Arabia. One of the seismic experiments investigated the water-table depth, and the other one attempted to detect the location of a buried fault. The seismic land data were inverted by WT and RT to generate the P-velocity tomograms, from which we can clearly identify the water table depth along the seismic survey line in the first example and the fault location in the second example.

Citation

Fu L, Hanafy SM (2017) Ray-tracing traveltime tomography versus wave-equation traveltime inversion for near-surface seismic land data. Interpretation 5: SO11–SO19. Available: http://dx.doi.org/10.1190/INT-2016-0210.1.

Acknowledgements

The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We are grateful to the sponsors of the Center for Subsurface Imaging and Modeling Consortium for their financial support. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST and the IT Research Computing Group. We thank them for providing the computational resources required for carrying out this work.