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Article
Peer-Review Record

Synthetic Data Generation for Deep Learning-Based Inversion for Velocity Model Building

Remote Sens. 2023, 15(11), 2901; https://doi.org/10.3390/rs15112901
by Apostolos Parasyris *, Lina Stankovic and Vladimir Stankovic
Reviewer 1:
Reviewer 2:
Remote Sens. 2023, 15(11), 2901; https://doi.org/10.3390/rs15112901
Submission received: 13 April 2023 / Revised: 25 May 2023 / Accepted: 30 May 2023 / Published: 2 June 2023
(This article belongs to the Special Issue Advances in Remote Sensing and Analysis of Slopes and Slope Failures)

Round 1

Reviewer 1 Report

Thank you for sharing your research. This was an interesting study and my comments can be found in the attached file. 

Comments for author File: Comments.pdf


Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper reads well and I like the topic presented here. 

I only have two minor suggestions. 

1. It would be great if the authors can connect the work presented here with some existing works on the same topic (e.g., Ren et al., 2021, Building Complex Seismic Velocity Models for Deep Learning Inversion, IEEE Access), and most importantly highlight the scientific advance.

2. It is a pity that I do not see a 3D model in this paper. It would be better to extend the current framework to 3D. If not possible, the authors need to clarify the difficulties. The paper I mentioned above, however, include a detailed workflow on generating 3D realistic model. 

 

 

Generally fine. 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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