Unstable Object Points during Measurements—Deformation Analysis Based on Pseudo Epoch Approach

Round 1

Reviewer 1 Report

Dear authors,

Please find attached.

Comments for author File: Comments.pdf

Author Response

Thank you very much for your review and valuable comments. Here is our response:

Thank you for your overall positive comments.

Major comments:

1. We implemented the explanation of how to assume the steering parameter value and referred to the mentioned paper.
2. We explain the computation algorithm of HLW estimation in a more detailed way.
3. The respective gradients should be close to zero enough not to change the parameters in the subsequent iterative steps more than the assumed tolerance. We introduced such a remark in the paper's revised version and added the respective references.
4. We consider the error of Monte Carlo simulations and the reference suggested by the reviewer.

Minor comments:

1. We have amended the translation following the reviewer’s suggestions. In fact, we have checked and improved the English in the whole manuscript. We considered the term “sub-epoch”; however, we suppose that such a term would suggest that the surveyor or analyst is aware of point displacements during measurements in a single epoch and somehow can predict the division of the observation set into subsets. Hence, we use the term “pseudo epoch,” which reflects the unpredictability of object point displacements during measurements.
2. We agree with the reviewer that the suggested subjects of further studies are interesting.
3. English has been improved.

Reviewer 2 Report

Comments for author File: Comments.pdf

Author Response

Thank you very much for your review and valuable comments. Here is our response:

1. The abstract has been improved following some suggestions from the reviewer.
2. We consider the sudden movements of the object points in the paper. The continuous movements require different measurement techniques and processing methods.
3. The paper addresses the problem of object points that shift during the measurements in one epoch. Thus, points are classified as “stable” when they remain undisplaced during measurements.

General comments:

1. M_split estimation can be applied for any observation set that is an unknown mixture of realizations of different random variables. Hence, it can also be used to address the problem mentioned by the reviewer.
2. In some sense, the approach suggested by the reviewer can be applied. However, dividing observations into good and bad data regions is always a problem. Statistical tests, which can be used in such a context, might be inefficient in many practical cases, especially when “unexpected” point displacements have relatively low magnitude.
3. Thank you for your comment.