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.
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).
Corresponding Authors: Su Qin (Email: suq@petrochina.com.cn)
E-mail: suq@petrochina.com.cn
About author: 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
Cite this article:
. Method for obtaining high-resolution velocity spectrum based on weighted similarity*[J]. APPLIED GEOPHYSICS, 2020, 17(2): 221-232.
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