A Study on the Imaging Method for the Channel Wave Dispersion Curve Variability Function
Round 1
Reviewer 1 Report
This is a very interesting contribution, The authors present a highly useful methodology. My only suggestion is that they could estimate how the error is reduced and the results are more stable, in comparison with other methodologies.
Good job!! Congratulations
Author Response
Comment 1 This is a very interesting contribution, The authors present a highly useful methodology. My only suggestion is that they could estimate how the error is reduced and the results are more stable, in comparison with other methodologies.
Good job!! Congratulations
Reply 1 Thank you, we are grateful for the suggestion. To be more clear and in accordance with the reviewer's concerns, we have revised the text based on your valuable comments, please see the changes in red marked in the revision. Thank you again for giving me the opportunity to revise. I hope this revision can meet your requirements.
“It can be seen from the comparative analysis of Figure. 6a and Figure. 6b as well as the mining verification results that the error of the variability function imaging method is small and more accurate. When the coal seam is stable, the inversion of the abnormal body is better than conventional tomography. The function construction can effectively reflect the combination characteristics of the time domain and frequency domain, and carry out variance imaging through the constraint of theoretical velocity on actual and the breakpoint feature of the abnormal body, with the error reduced and the results are more stable.”
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper describes a methodology based on the analysis of seismic data for imaging the underground velocity structure based on the inversion of channel wave dispersion curves. The introduction well illustrates the problems and the state-of-the art in the field. The methods of data analysis and the processing techniques are clearly presented. The precision analysis is very robust, allowing to constrain the advantages and the limits of the technique. The travel-time tomography inversion procedure is not adequateely described and I believe this point should be better clarified and improved.
The paper is of high quality, and it is generally clearly written and illustrated. The conclusions are consistent with the data analysis and the references are appropriate. I recommend this paper for publication in Minerals after minor revision.
Author Response
Comment 2. This paper describes a methodology based on the analysis of seismic data for imaging the underground velocity structure based on the inversion of channel wave dispersion curves. The introduction well illustrates the problems and the state-of-the art in the field. The methods of data analysis and the processing techniques are clearly presented. The precision analysis is very robust, allowing to constrain the advantages and the limits of the technique. The travel-time tomography inversion procedure is not adequately described and I believe this point should be better clarified and improved.
Reply 2: Thank you, we are grateful for the suggestion. We have revised the text based on your valuable comments and added the relevant references, please see the changes in red marked in the revision. I hope to meet your requirements.
“When channel waves pass through an abnormal body, the dispersion curves have poorer continuity and are not smooth, making impossible the accurate determination of the travel time and leading to difficulty in determining the travel time based on a fixed frequency. In channel wave tomography, the travel time (velocity) of the Airy phase or the travel time (velocity) information of a certain frequency seismic phase picked up from the dispersion curve. The inversion calculation algorithm using tomography is as follows[25-27]: Back projection method (BG), conjugate gradient method (CG), Least square QR-factorization(LSQR), Algebraic reconstruction technique (ART), simultaneous iterative reconstruction technique(SIRT), etc. The results of these inversion methods are only relative, and the result values are through to the velocity information of the dispersion curve within the thickness range of coal seam for theoretical inversion. However, it is difficult to determine the degree of influence of geological anomalies on the dispersion characteristics.In actual detection work, determining travel time on complex coal faces is an important link that causes issues for processing personnel. Lacking a unified standard for determining velocity, the imaging results are inconsistent and inaccurate[28-30]. To solve the aforementioned imaging problems, this paper proposes a function that is based on the quantitative description and calculates the variation degrees of dispersion curves and utilizes the tomography method to identify abnormal bodies.”
Author Response File: Author Response.pdf
