Stability and uncertainty assessment of geoelectrical resistivity model parameters: a new hybrid metaheuristic algorithm and posterior probability density  function approach

Sarkar, Kuldeep; Tiwari, Jit V.; Singh, Upendra K.

doi:https://doi.org/10.5194/npg-31-7-2024

Articles | Volume 31, issue 1

https://doi.org/10.5194/npg-31-7-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-31-7-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 31, issue 1

Research article

|

10 Jan 2024

Research article |

| 10 Jan 2024

Stability and uncertainty assessment of geoelectrical resistivity model parameters: a new hybrid metaheuristic algorithm and posterior probability density function approach

Kuldeep Sarkar, Jit V. Tiwari, and Upendra K. Singh

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Final revised paper (published on 10 Jan 2024)
Preprint (discussion started on 23 Nov 2022)

Interactive discussion

Status: closed

RC1:
'Comment on npg-2022-13', Stanley Raj, 26 Dec 2022
Comments to Author

I appreciate the authors effort to attempt a new hybrid metaheuristic algorithm for inverting geoelectrical data

Few things need to be addressed before going for publication

Many neural networks algorithm works better than other algorithm. What is the significance of using vPSOGWO optimization algorithm.

What about the computational time and memory for using this algorithm in comparison with other conventional methods?

Note down the advantages, disadvantages and constraints of the algorithm.

How principle of equivalence problem can be avoided by using this algorithm?

Which model of the algorithm works well and give more performance – Forward-Inverse modelling?

Random weights have been fixed for working out the algorithm. Have the authors applied any specific logic in fixing the weights or else any meaning approach implemented? Clarify.

What are the types of noises involved in training the algorithm? What about SNR?
Citation: https://doi.org/10.5194/npg-2022-13-RC1
- AC1:
  'Reply on RC1', Upendra K Singh, 06 Jan 2023
  
  Dear Dr. Stanley Raj, Reviewer
  Nonlinear Processes in Geophysics
  First of all we would like to wish a happy and prosperous healthy new year to you and your family. We would appreciate your nice and very useful quarries/comment on our submitted manuscript entitled “ Inversion, Assessment of Stability and Uncertainty of Geoelectric Sounding data using a New Hybrid Meta-heuristic algorithm and Posterior Probability Density Function Approach” by Kuldeep Sarkar and Upendra K. Singh.
  We try our level best to furnish all those useful comment one-by-one here, which improve our knowledge. Still if you have some other comments for improving our manuscript for publication in reputed journal like Nonlinear Processes in Geophysics (NPG), we are very happy to carry out in future. The reply to Reviewer's comments/quaries are attached in Pdf format.
  With kind regards
  Upendra K. Singh
  
  Citation: https://doi.org/10.5194/npg-2022-13-AC1
  - RC2: 'Reply on AC1', Stanley Raj, 06 Jan 2023
    
    Authors reply to the initial discussions are satisfactory and I recommend the article to proceed for further processing of publication
    Thank you
    
    Citation: https://doi.org/10.5194/npg-2022-13-RC2
    
    CC1: 'Reply on RC2', Upendra K Singh, 07 Jan 2023
    
    To
    Editor inchief
    First and foremost we woulk like really thanks to editorial team for organising the discussion session between us and reviwers for scientific discussion. This discussion help to improve our acknowledge. Again we are thankful to you.
    
    With kind regards
    Upendra K. Singh
    
    Reply to reviewer
    Let us heartly thanks for reviewer satisfactions. We are very happy that this discussion has enhance our scientific knowledge. Thanks for again for your recommendation for further publication our manuscript.
    With kind regards
    Upendra K Singh
    
    Citation: https://doi.org/10.5194/npg-2022-13-CC1
RC3:
'Comment on npg-2022-13', Khalid Essa, 10 Jan 2023

The manuscript is well established and well written. So, I strongly recommend accepting it for publication and do not need any revision.

Citation: https://doi.org/10.5194/npg-2022-13-RC3
- CC2: 'Reply on RC3', Upendra K Singh, 10 Jan 2023
  
  To
  Editor Inchief
  First and foremost, we would like to express our sincere gratitude to the editorial team for setting up the meeting between us and the reviewers for a scientific conversation. This conversation has improved our understanding. We want to thank you one again.
  With kind regards
  Upendra K. Singh
  
  Reply to reviewer
  Please accept our sincere gratitude for the positive reviews and recomendation for publication. We are pleased that this conversation has improved our scientific understanding. Once again, thank you for your advice to publish our paper further.
  With kind regards
  
  Citation: https://doi.org/10.5194/npg-2022-13-CC2
AC2: 'Comment on npg-2022-13', Upendra K Singh, 21 Jan 2023

As required the revised version of the manuscript which has been submitted through email because there is no option given here to upload the revised versio so kindly look this matter and you may kindly update please.

Citation: https://doi.org/10.5194/npg-2022-13-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Upendra K Singh on behalf of the Authors (25 Oct 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (26 Oct 2023) by Christian Franzke

AR by Upendra K Singh on behalf of the Authors (26 Oct 2023)

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Upendra K Singh on behalf of the Authors (04 Jan 2024) Author's adjustment Manuscript

EA: Adjustments approved (05 Jan 2024) by Christian Franzke

Short summary

We evaluated three meta-heuristic algorithms using resistivity data. An enormous solution is assessed and the best-fitted solutions are chosen. The posterior probability density function with a 68.27 % confidence interval, a mean and posterior solution, and correlation and covariance matrix were calculated for the assessment of the uncertainty, stability, and mean model. We found that vPSOGWO provides reliable and consistently better results that are correlated well with borehole information.