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I'm very glad to get the so wonderful codes. I try to implement other PML methods some days ago, but the PML method in deepwave have good enough result. So I give up this idea. I have read most codes in the deepwave. I have same questions about it.
question 1,
Is that possible to extend to wave case from acoustic case? I mean using velocity-stress form of the elastodynamic equations to generate seismic data and then we can inverse vp,vs and ρ using deepwave.
question 2,
Is the deepwave using RNN? I have read the paper that you wrote at 2018 "Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques". In this paper, you believe conventional seismic FWI can be constructed as a RNN, but I can't found any codes in deepwave about RNN. So, is that true that the forward function of finite difference is the process of RNN?
The text was updated successfully, but these errors were encountered:
Dear Alan,
I'm very glad to get the so wonderful codes. I try to implement other PML methods some days ago, but the PML method in deepwave have good enough result. So I give up this idea. I have read most codes in the deepwave. I have same questions about it.
question 1,
Is that possible to extend to wave case from acoustic case? I mean using velocity-stress form of the elastodynamic equations to generate seismic data and then we can inverse vp,vs and ρ using deepwave.
question 2,
Is the deepwave using RNN? I have read the paper that you wrote at 2018 "Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques". In this paper, you believe conventional seismic FWI can be constructed as a RNN, but I can't found any codes in deepwave about RNN. So, is that true that the forward function of finite difference is the process of RNN?
The text was updated successfully, but these errors were encountered: