Abstract
Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequency domain while using only the shots that are positively correlated with frequency. When using low-frequency data, the method considers only a small number of shots and raw data. More shots are used with increasing frequency. The random-in-group subsampling method is used to rotate the shots between iterations and avoid the loss of shot information. By reducing the number of shots in the inversion, we decrease the computational cost. There is no crosstalk between shots, no noise addition, and no observational limits. Numerical modeling suggests that the proposed method reduces the computing time, is more robust to noise, and produces better velocity models when using data with noise.
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We are grateful to all reviewers for their constructive comments and suggestions. We also thank the editorial department of Applied Geophysics for their work.
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The research was financially supported by the Fundamental Research Funds for the Central Universities (No. 201822011), the National Natural Science Foundation of China (No. 41674118) and the National Science and Technology Major Project (No. 2016ZX05027002).
Shi Cai-Wang received his B.S. in Geoinformation Science and Technology in 2015 from the Ocean University, China. He is presently enrolled in the M.S. program of Geodetection and Information Technology in the same institute. His interests are forward modeling and full-waveform inversion.
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Shi, CW., He, BS. Multiscale full-waveform inversion based on shot subsampling. Appl. Geophys. 15, 261–270 (2018). https://doi.org/10.1007/s11770-018-0669-6
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DOI: https://doi.org/10.1007/s11770-018-0669-6