Wave-equation Traveltime Inversion with Multi-Frequency Bands: Synthetic and Land Data Examples

Han Yu, Sherif M. Hanafy, Gerard T. Schuster

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A wave-equation traveltime inversion method with multifrequency bands is proposed to invert for the shallow or intermediate subsurface velocity distribution. Similar to the classical wave equation traveltime inversion, this method searches for the velocity model that minimizes the squared sum of the traveltime residuals using source wavelets with progressively higher peak frequencies. Wave-equation traveltime inversion can partially avoid the cycle skipping problem by recovering the low-wavenumber parts of the velocitymodel. However, we also utilize the frequency information hidden in the traveltimes for obtaining a more highly resolved tomogram. Therefore, we employ different frequency bands when calculating the Fréchet derivatives so that tomograms with better resolution can be reconstructed. Results are validated by the zero offset gathers from the raw data associated with moderate geometrical irregularities. The improved wave-equation traveltime method is robust and merely needs a rough estimate of the startingmodel. Numerical tests on both the synthetic and field data sets validate the above claims.
Original languageEnglish (US)
Pages (from-to)B305-B315
Number of pages1
JournalGEOPHYSICS
Volume83
Issue number6
DOIs
StatePublished - Oct 11 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank the 2018 sponsors of the Center for Subsurface Imaging and Modeling 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. The authors are also grateful for the professional comments from Dr. Chuen Hon Arthur Cheng and Prof. M. A. H. Zuberi. This work is also sponsored by the National Natural Science Foundation of China (grant no. 11501302), and the China Postdoctoral Science Foundation (grant nos. 2017M611883 and 2018T110531).

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