Questions On Primary Conversions And Multiples

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Table 2 present arrival times of primary conversions and multiples, but it is challenging to see those multiples in the RFs and look like noise. Additionally, the authors do not use such a long window in the inversion.

Methodology: The authors try to summarize three different methods, but few statements are not correct and others give different meaning. They can move “Receiver Functions” section to the observations section as those are really the secondary observations obtained using the deconvolution of seismograms. I present here few examples of problematic statement.

The authors say “Gaussian width of 2.5 Hz is applied”, but the Gaussian width does not have any unit. The authors can check Ammon’s webpage for more details
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Importantly, this is how we solve the most of the geophysical inverse problem and works well with some limitations. It is also not clear why the authors do not apply Neighborhood Algorithm (NA) for the Moho depth and other interfaces.

The authors apply NA, which has two important tuning parameters. Those parameters help to achieve faster convergence. It is important to note that those parameters depend on the problem, but the authors never mention how those parameters were tuned and convergence was decided.

The authors also mention that they use 24 parametric bounds of Shibhutani et al. (1996), Clitheroe et al., 2000, Fontaine et al. 2013 with six layers without any proper justification. My understanding is that each layer has 4 parameters (thickness, S-velocity, Vp/Vs ratio, and density). However, the authors mention that the layer properties are represented by thickness, S-velocity and Vp/Vs ratio. In that case, is the density fixed to some values? Additionally, why do they decide to fix it to six layers, why not five/seven?

The authors say in line 240 that “the set of 24 parameters in the inversion are relatively well constrained” and not sure relative to what?

Section 4.4: Mathematics on the H-K stacking is not required unless the authors have done this differently. Mathematics also suffers from the same notations used for different variables (e.g., variance and Poisson’s ratio). Moreover, the authors derive relations for standard deviation of

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