Abstract
There are lots of low wavenumber noises in the gradients of time domain full waveform inversion (FWI), which can seriously reduce the accuracy and convergence speed of FWI. Thus, we introduce an angle-dependent weighting factor to precondition the gradients so as to suppress the low wavenumber noises when the multi-scale FWI is implemented in the high frequency. Model experiments show that the FWI based on the gradient preconditioning with an angle-dependent weighting factor has faster convergence speed and higher inversion accuracy than the conventional FWI. The tests on real marine seismic data show that this method can adapt to the FWI of field data, and provide high-precision velocity models for the actual data processing.
Similar content being viewed by others
References
Alkhalifah, T., 2015a. Scattering angle based filtering of the waveform inversion gradients. Geophysical Journal International, 200: 363–373.
Alkhalifah, T., 2015b. Conditioning the full waveform inversion gradient to welcome anisotropy. Geophysics, 80: R111–R122.
Alkhalifah, T., 2016. Full model wavenumber inversion (FMWI): An emphasize on the appropriate wavenumber continuation. Geophysics, 81: R89–R98.
Boonyasiriwat, C., Valasek, P., Routh, P., Cao, W., and Macy, B., 2009. An efficient multi-scale method for time-domain waveform tomography. Geophysics, 74(6): WCC59–WCC68.
Bunks, C., Fatimetou, M. S., Zaleski, S., and Chavent, G., 1995. Multi-scale seismic waveform inversion. Geophysics, 60(5): 1457–1473.
Chen, S., and Chen, G., 2016. Full waveform inversion of time second order integral wavefield. Journal of Geophysics, 59(10): 3765–3776.
Fang, X., Niu, F., and Wu, D., 2018. Least-squares reverse-time migration enhanced with the inverse scattering imaging condition. Chinese Journal of Geophysics, 61(9): 3770–3782.
Liu, D., Huang, J., and Wang, Z., 2020. Convolution-based multi-scale envelope inversion. Petroleum Science, 2: 352–362.
Masmoudi, N., and Alkhalifah, T., 2018. Full waveform inversion in acoustic orthorhombic media and application to a North Sea data set. Geophysics, 83: C179–C193.
Miller, D., Oristaglio, M., and Beylkin, G., 1987. A new slant on seismic imaging: Migration and integral geometry. Geophysics, 52: 943–964, DOI: https://doi.org/10.1190/1.1442364.
Oh, J., Kalita, M., and Alkhalifah, T., 2018. 3D elastic full waveform inversion using P-wave excitation amplitude: Application to real OBC data. Geophysics, 83(2): R129–R140.
Pratt, R. G., and Shipp, R. M., 1999. Seismic waveform inversion in the frequency domain. Part 2: Fault delineation in sediments using crosshole data. Geophysics, 64(3): 902–914.
Song, P., Tan, J., Liu, Z. L., Zhang, X. B., Liu, B. H., Yu, K. B., et al., 2019. Time-domain full waveform inversion using the gradient preconditioning based on transmitted wave energy. Journal of Ocean University of China, 18(4): 859–867.
Tarantola, A., 1984. Inversion of seismic reflection data in the acoustic approximation. Geophysics, 49(8): 1259–1266.
Tarantola, A., 1986. A strategy for nonlinear elastic inversion of seismic reflection data. Geophysics, 51(10): 1893–1903.
Thomas, A. D., and Graham, A. W., 2011. RTM angle gathers using Poynting vectors. 81th Annual International Meeting, SEG, Expanded Abstracts. Las Vegas, 3109–3113.
Wang, G., Alkhalifah, T., and Wang, S., 2020. Enhancing low-wavenumber information in reflection waveform inversion by the energy norm born scattering. IEEE Geoscience and Remote Sensing Letters, 19: 8000205, 10.1109/LGRS.2020.3019536.
Wang, Z. Q., and Han, L., 2018. Vertical total variation constrained full waveform inversion based on hinge loss function. Journal of Geophysics, 61(4): 1460–1470 (in Chinese with English abstract).
Wu, R., Luo, J., and Wu, B., 2014. Seismic envelope inversion and modulation signal model. Geophysics, 79(3): WA13–WA24.
Wu, Z., and Alkhalifah, T., 2017. Selective data extension for full-waveform inversion: An efficient solution for cycle skipping. Geophysics, 83(3): R201–R211, DOI: https://doi.org/10.1190/geo2016-0649.1.
Yang, K., and Zhang, J., 2018. Least-squares reverse time migration with an angle-dependent weighting factor. Geophysics, 83(3): 299–310.
Yao, G., da Silva, N. V., Warner, M., and Kalinicheva, T., 2018. Separation of migration and tomography modes of full-waveform inversion in the plane wave domain. Journal of Geophysical Research: Solid Earth, 123(2): 1486–1501.
Yao, G., Silva Nuno, V., and Wu, D., 2019. Reflection-waveform inversion regularized with structure-oriented smoothing shaping. Pure and Applied Geophysics, 176(12): 5315–5335.
Zhang, Z., and Alkhalifah, T., 2020. High-resolution reservoir characterization using deep learning aided elastic full-waveform inversion: The North Sea field data example. Geophysics, 85: WA137–WA146.
Zhang, Z., Huang, L., and Lin, Y., 2012. A wave-energy-based precondition approach to full-waveform inversion in the time domain. 82th Annual International Meeting, SEG, Expanded Abstracts. San Antonio, 1–5.
Zhang, Z., Lin, Y., and Huang, L., 2011. Full-waveform inversion in the time domain with an energy-weighted gradient. 81th Annual International Meeting, SEG, Expanded Abstracts. Las Vegas, 2772–2776.
Acknowledgements
This research is jointly funded by the National Natural Science Foundation of China (No. 42074138), the Wenhai Program of the S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (No. 2021WHZZB0700), and the Major Scientific and Technological Innovation Project of Shandong Province (No. 2019JZZY010803).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xia, D., Song, P., Li, X. et al. Time Domain Full Waveform Inversion Based on Gradient Preconditioning with an Angle-Dependent Weighting Factor. J. Ocean Univ. China 21, 1479–1486 (2022). https://doi.org/10.1007/s11802-022-4956-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11802-022-4956-8