Time-Domain Full Waveform Inversion Using the Gradient Preconditioning Based on Transmitted Wave Energy
- 43 Downloads
The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of this approach is prone to be influenced by the energy of reflected waves. To tackle this problem, the paper proposes a new gradient preconditioning method based on the energy of transmitted waves. The approach scales the gradient through a precondition factor, which is calculated by the ‘approximate transmission wavefield’ simulation based on the nonreflecting acoustic wave equation. The method requires no computing nor storing of the Hessian matrix and its inverse matrix. Furthermore, the proposed method can effectively eliminate the effects of geometric spreading and disproportionality in the gradient illumination. The results of model experiments show that the time-domain full waveform inversion (FWI) using the gradient preconditioning based on transmitted wave energy can achieve higher inversion accuracy for deep high-velocity bodies and their underlying strata in comparison with the one using the gradient preconditioning based on seismic wave energy. The field marine seismic data test shows that our proposed method is also highly applicable to the FWI of field marine seismic data.
Key wordsfull waveform inversion gradient preconditioning transmitted wave nonreflecting acoustic wave equation
Unable to display preview. Download preview PDF.
The authors appreciate the support of the NSFC-Shandong Joint Fund for Marine Science Research Centers (No. U1606401), the National Natural Science Foundation of China (Nos. 41574105 and 41704114), the National Science and Technology Major Project of China (No. 2016ZX05027-002) and Taishan Scholar Project Funding (No. tspd20161007).
- Diaz, E., 2011. Fast full waveform inversion with random shot decimation. 81st Annual International Meeting, SEG. San Antonio, 2804–2808.Google Scholar
- Etgen, J. T., 1986a. High-order finite-difference reverse time migration with the 2-way non-reflecting wave equation. SEP Report, 48: 133–146.Google Scholar
- Etgen, J. T., 1986b. Prestack reverse time migration of shot profiles. SEP Report, 50: 151–169.Google Scholar
- Guitton, A., Ayeni, G., and Gonzolez, G., 2010. A preconditioning scheme for full waveform inversion. 80th Annual International Meeting, SEG. Denver, 1008–1012.Google Scholar
- Mao, J., Wu, R., and Wang, B., 2012. Multiscale full waveform inversion using GPU. 82nd Annual International Meeting, SEG. Houston, 1–7.Google Scholar
- Song, P., 2013. The time-domain full waveform inversion based on high-performance multi-card GPU computing. PhD thesis. Ocean University of China.Google Scholar
- Wang, B., and Gao, J. H., 2011. CUDA-base acceleration of full waveform inversion on GPU. 81st Annual International Meeting, SEG. San Antonio, 2528–2533.Google Scholar
- Zhang, S. Q., Liu, C. C., Han, L. G., and Yang, X. C., 2013. Frequency multi-scale full waveform inversion based on L-BFGS algorithm simultaneous sources approach. Journal of Jilin University (Earth Science Edition), 43 (3): 1004–1012 (in Chinese with English abstract).Google Scholar
- Zhang, Z. G., Huang, L. J., and Lin, Y. Z., 2012a. A wave-energy-based precondition approach to full-waveform inversion in the time domain. 82nd Annual International Meeting, SEG. Houston, 1–5.Google Scholar
- Zhang, Z. G., Huang, L. J., and Lin, Y. Z., 2012b. Double-difference elastic-waveform inversion with weighted gradients for monitoring egs reservoirs. 37th Workshop on Geothermal Reservoir Engineering Stanford University. Stanford, SGP-TR-194.Google Scholar
- Zhang, Z. G., Lin, Y. Z., and Huang, L. J., 2011. Full-waveform inversion in the time domain with an energy-weighted gradient. 81st Annual International Meeting, SEG. San Antonio, 2772–2776.Google Scholar