Abstract
A visco-acoustic wave-equation traveltime inversion method is presented that inverts for the shallow subsurface velocity distribution. Similar to the classical wave equation traveltime inversion, this method finds the velocity model that minimizes the squared sum of the traveltime residuals. Even though, wave-equation traveltime inversion can partly avoid the cycle skipping problem, a good initial velocity model is required for the inversion to converge to a reasonable tomogram with different attenuation profiles. When Q model is far away from the real model, the final tomogram is very sensitive to the starting velocity model. Nevertheless, a minor or moderate perturbation of the Q model from the true one does not strongly affect the inversion if the low wavenumber information of the initial velocity model is mostly correct. These claims are validated with numerical tests on both the synthetic and field data sets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 103-112 |
| Number of pages | 10 |
| Journal | Journal of Applied Geophysics |
| Volume | 151 |
| DOIs | |
| State | Published - Feb 23 2018 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: We would like to thank the 2017 sponsors of the CSIM Consortium (http://csim.kaust.edu.sa/web/) for their support. The research reported in this paper was also 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 for carrying out this work. The authors are also grateful for the professional comments from Prof. Gerard T. Schuster (KAUST), Gaurav Dutta (CGG, Houston), and other anonymous CSIM members. We are grateful for the financial support of National Natural Science Foundation of China (grants: 11501302, 61571233 and 91646116), the China Postdoctoral Science Foundation Funded Project (grant: 2017M611883).