Dear Dr. Yu,

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Academic Editor

PLOS ONE

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[This research was supported by the Liaoning Provincial Department of Education under Grant No. JYTQN2023207 (received by T.Y.) and the National Natural Science Foundation of China under Grant No. 41790453 (received by T.Y.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.].

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[This work was supported by the Basic Scientific Research Projects of Liaoning Provincial Department of Education (grant no. JYTQN2023207) and the National Natural Science Foundation of China (grant no. 41790453). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.]

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[This research was supported by the Liaoning Provincial Department of Education under Grant No. JYTQN2023207 (received by T.Y.) and the National Natural Science Foundation of China under Grant No. 41790453 (received by T.Y.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.]

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Additional Editor Comments :

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: The paper presentation is very poor. However, the following several issues need to be clarified, and it should be noted that the manuscript can only be considered for acceptance after major revisions to address these concerns:

1. Write the full form of the GR, CNL, DEN, AC, RLA5 in the abstract

2. Remove the keyword random forest’

3. Introduction is written very poor in terms of presentation.

4. Please provide supporting reference for the line number 55-57.

5. Line number 64-68 should be shifted to the discussion section.

6. Please go through the recent findings on lithology prediction using ML approaches, and cite them (e.g., (1) Prajapati, R., Mukherjee, B., Singh, U.K. et al. Machine learning assisted lithology prediction using geophysical logs: A case study from Cambay basin. J Earth Syst Sci 133, 108 (2024). https://doi.org/10.1007/s12040-024-02326-y

(2) Mukherjee, B., Sain, K. Vertical lithological proxy using statistical and artificial intelligence approach: a case study from Krishna-Godavari Basin, offshore India. Mar Geophys Res 42, 3 (2021). https://doi.org/10.1007/s11001-020-09424-8

(3) Mukherjee, B., Kar, S. & Sain, K. Machine Learning Assisted State-of-the-Art-of Petrographic Classification From Geophysical Logs. Pure Appl. Geophys. 181, 2839–2871 (2024). https://doi.org/10.1007/s00024-024-03563-4

(4) Banerjee, A., Mukherjee, B. & Sain, K. Machine learning assisted model based petrographic classification: a case study from Bokaro coal field. Acta Geod Geophys 59, 463–490 (2024). https://doi.org/10.1007/s40328-024-00451-0)

7. Please go through the above paper and try to rephrase the introduction section.

For the geological setting please provide a generalised litho-stratigraphic section, as presented in https://doi.org/10.1007/s00024-024-03563-4

8. Hyperparameter tunning part is missing in the Fig.3.

9. Please provide the proper citation for line number 226-233.

10. Please provide the ROC curves (as given in https://doi.org/10.1007/s00024-024-03563-4)

11. Please provide the Training and Test accuracies through histogram analysis.

12. Details content is required for the Line number 333 to 347.

Reviewer #2: This study tackles the challenging task of volcanic‐rock lithology classification by introducing an Enhanced Random Forest framework that synergistically combines Borderline‐SMOTE for imbalance correction, C4.5 decision trees for feature refinement, and Kendall’s coefficient of concordance for ensemble optimization. The approach is interesting, and the manuscript merits publication after revision. The specific issues to be addressed are outlined below.

1. The manuscript reports high overall accuracy and presents detailed confusion matrices (Fig. 4), yet only describes which lithologies were misclassified. The authors should elucidate the geological and petrophysical factors underlying these misclassifications. Moreover, replacing the numeric labels (1–18) on the confusion matrix with abbreviated lithology names (e.g., “Basalt,” “Trachyte,” “Andesite”) would markedly improve readability.

2. A more comprehensive survey of prevailing machine-learning approaches for lithology classification, including their strategies for addressing class imbalance, would strengthen the Introduction and contextualize the proposed method.

3. Table 4 tabulates each model’s optimal hyperparameters but omits the parameter search ranges. For reproducibility, please specify the tuning intervals and search methodology employed.

4. The integration of C4.5 decision trees and Kendall’s coefficient of concordance to refine the Random Forest is innovative, but its implementation remains abstract. A clearer, step-by-step account of how these components interact within the algorithmic workflow is necessary.

5. The blind test described in the “Model Application Effectiveness” section is pivotal for assessing generalization, yet the provenance of these blind well test data is unspecified. Please identify the data source and any pre-processing performed.

6. A thorough review of the manuscript is recommended to rectify minor grammatical and typographical errors—particularly in punctuation, article usage, and pluralization—to ensure a polished and professional presentation.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes:  Bappa Mukherjee

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Enhanced random forest with geologically-informed feature optimization for complex volcanic rock lithology identification: a case study in the Wangfu Fault Depression, Songliao Basin

PONE-D-25-28721R1

Dear Dr. Yu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Hu Li

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Authors have done the necessery corrections as suggested. I feel in the current form it is ready for publication.

Reviewer #2: The authors have addressed the previous review comments and revised the manuscript accordingly. The responses are detailed and the modifications are well implemented. The revised version now meets the requirements for publication.

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

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