Abstract
Seismic wave velocity is one of the most important processing parameters of seismic data, which also determines the accuracy of imaging. The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity, producing the velocity spectrum by superposing energy or similarity coefficients. In this method, however, the sensitivity of the semblance spectrum to change of velocity is weak, so the resolution is poor. In this paper, to solve the above deficiencies of conventional velocity analysis, a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed. By introducing two weighting functions, the resolution of the similarity spectrum in time and velocity is improved. Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums; at the same time, the method shows good noise-resistibility.
Similar content being viewed by others
References
Biondi, B. L., and Kostov, C., 1989, High resolution velocity spectra using eigenstructure methods: Geophysics, 54(7), 832–842.
Barros, T., Lopes, R., and Tygel, M., 2015, Implementation aspects of eigen decomposition-based high-resolution velocity spectra: Geophysical Prospecting, 63(1), 99–115.
Chen, H. F., Li, X. Y., Qian, Z. P., et al., 2016, Prestack migration velocity analysis based on simplified two-parameter moveout equation: Applied Geophysics, 13(1), 135–144.
Chen, Y. K., 2018, Automatic velocity analysis using high-resolution hyperbolic Radon transform: Geophysics, 83(4), A53–A57.
Claerbout, J. F., 1985, Imaging the earth’s interior: Blackwell Scientific Publications, Boston.
De Vries, D., and Berkhout, A.J., 1984, Velocity analysis based on minimum entropy: Geophysics, 49(12), 2132–2142.
Ebrahimi, S., Kahoo, A. R., Porsani, M. J., et al., 2016, Obtaining high-resolution velocity spectra using weighted semblance: Exploration Geophysics, 48(3), 210–218.
Gabriel, F. O., and Rahul, S., 2018, Seismic velocity estimation: A deep recurrent neural-network approach: Geophysics, 85(1), U21–U29.
Gong, X. B., Wang, S. C., and Zhang, T. Z., 2016, Velocity analysis using high-resolution semblance based on sparse hyperbolic Radon transform: Journal of Applied Geophysics, 134, 146–152.
Key, S. C., and Smithson, S. B., 1990, New approach to seismic-reflection event detection and velocity determination: Geophysics, 55(8), 1057–1069.
Laub, A. J., 2005, Matrix analysis for scientists and engineers: Society for Industrial and Applied Mathematics, Philadelphia.
Liu, G. C., Li, C., Liu, X. Y., et al., 2018, Automatic stacking-velocity estimation using similarity-weighted clustering: Geophysical Prospecting, 66, 649–663.
Luo, S., and Hale, D., 2012, Velocity analysis using weighted semblance: Geophysics, 77(2), U15–U22.
Masaya, S., and Verschuur, D. J. E., 2018, Iterative reflectivity-constrained velocity estimation for seismic imaging: Geophysical Journal International, 214, 1–13.
Mauricio, A. P., Stuart, F., and Manuel, F., 2019, Deep learning-driven velocity model building workflow: The Leading Edge, 38(11), 872a1–872a9.
Mostafa, A., and Ali, G., 2018, Automatic nonhyperbolic velocity analysis by polynomial chaos expansion: Geophysics, 83(6), U79–U88.
Park, M. J., and Sacchi, M. D., 2020, Automatic velocity analysis using convolutional neural network and transfer learning: Geophysics, 85(1), V33–V43.
Schuba, C. N., Schuba, J. P., Gray, G. G., et al., 2019, Interface-targeted seismic velocity estimation using machine learning: Geophysical Journal International, 218, 45–56.
Sheriff, R. E., and Geldart, L. P., 1995, Exploration seismology: Cambridge University Press, London.
Ursin, B., da Silva, M. G., and Porsani, M. J., 2013, Generalized semblance coefficients using singular value decomposition: 13th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 1544–1549.
Wang, Z., 2014, Joint inversion of P-wave velocity and Vp-Vs ratio: imaging the deep structure in NE Japan: Applied Geophysics, 11(2), 119–127.
Xie, J. F., Xie, J. F., Sun, C. Y., Wang, X. M., et al., 2017, The multi-criteria velocity analysis of seismic data: Geophysical and Geochemical Exploration, 41(3), 513–520.
Zhang, P., and Lu, W. K., 2016, Automatic time-domain velocity estimation based on an accelerated clustering method: Geophysics, 81(4), U13–U23.
Acknowledgements
We thank the joint funding of the national key R&D plan “High performance application software system and demonstration of energy exploration for stage-oriented computing” (No. 2017YFB0202905) and China Petroleum Corporation Technology Management Department “Deep-ultra-deep weak signal enhancement technology based on seismic physical simulation experiments” (No. 2017-5307073-000008-01). We also thank the reviewers and editors for their constructive comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
The research is jointly funded by the National Key Research and Development Plan (No. 2017YFB0202905) and China Petroleum Corporation Technology Management Department “Deep-ultra-deep weak signal enhancement technology based on seismic physical simulation experiments” (No. 2017-5307073-000008-01).
Xu Xingrong, Senior Engineer of Geophysical Prospecting, currently works at the Institute of Data Processing, Research Institute of Petroleum Exploration & Development-Northwest (NWGI), PetroChina. The author graduated from Jilin University with a master’s degree in earth exploration and information technology in 2009, and is currently mainly engaged in seismic data processing method research and software development. Email: xu_xr@petrochina.com.cn
Rights and permissions
About this article
Cite this article
Xu, XR., Su, Q., Xie, JF. et al. Method for obtaining high-resolution velocity spectrum based on weighted similarity. Appl. Geophys. 17, 221–232 (2020). https://doi.org/10.1007/s11770-020-0816-8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11770-020-0816-8