the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inversion, Assessment of Stability and Uncertainty of Geoelectric Sounding data using a New Hybrid Meta-heuristic algorithm and Posterior Probability Density Function Approach
Kuldeep Sarkar
Upendra K. Singh
Abstract. Estimating a reliable subsurface resistivity structure using conventional techniques is challenging due to the nonlinear nature of the inverse problems. The performance of the inversion techniques can be pretty ambiguous based on the optimal error. Although traditional methods have proven to be quite effective. The impact of the constraints accessible from the borehole is examined for further assessment and enhance the algorithm’s effectiveness. The vPSOGWO is a new approach based on model search space without any prior information. This new strategy describes the hybridization of the particle swarm optimizer (PSO) with the grey wolf optimizer (GWO). To understand the efficiency and novelty of the algorithm, it has been validated on two different kinds of synthetic resistivity data with various sets of noise and finally applied on three field datasets of different geological terrains. The analyzed results suggest that the subsurface resistivity model shows considerable uncertainty. Thus, it is superior to examine the histograms and posterior probability density functions (PDF) of such solutions for exemplifying the global solution. PDF with 68.27 % CI selects a region with a higher probability. Therefore, the inverted models are used to estimate the mean global solution and the most negligible uncertainties, where the mean global solution represents the best solution. Our vPSOGWO inverted outcomes have been proven to be more accurate than classic PSO, GWO and state-of-art variant of classic approaches. As a results, this novel method plays a vital role in DC data inversion effectively.
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Kuldeep Sarkar and Upendra K. Singh
Status: open (extended)
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RC1: 'Comment on npg-2022-13', Stanley Raj, 26 Dec 2022
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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?
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AC1: 'Reply on RC1', Upendra K Singh, 06 Jan 2023
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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
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RC2: 'Reply on AC1', Stanley Raj, 06 Jan 2023
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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
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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
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CC1: 'Reply on RC2', Upendra K Singh, 07 Jan 2023
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RC2: 'Reply on AC1', Stanley Raj, 06 Jan 2023
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RC3: 'Comment on npg-2022-13', Khalid Essa, 10 Jan 2023
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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
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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
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CC2: 'Reply on RC3', Upendra K Singh, 10 Jan 2023
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AC2: 'Comment on npg-2022-13', Upendra K Singh, 21 Jan 2023
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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
Kuldeep Sarkar and Upendra K. Singh
Kuldeep Sarkar and Upendra K. Singh
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