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In this paper, we will explore the possibility of synthesizing the low-frequency data from the high-frequency data. The synthesized low-frequency data are used to improve the full-waveform inversion (FWI). Unlike all previously methods, to the best of our knowledge, this is the first attempt to utilize a data driven approach to solve the problem. We propose to learn the low wavenumber information in FWI via the Deep Inception based Convolutional Networks. Once the deep learning network is sufficiently trained, the network can be used to predicted the low-frequency data with high accuracy on a completely different unknown velocity model. In the end, we validate the quality of the predicted low-frequency data and the robustness of this deep learning approach. Presentation Date: Wednesday, October 17, 2018 Start Time: 1:50:00 PM Location: 204B (Anaheim Convention Center) Presentation Type: Oral
Jin et al. (Mon,) studied this question.