Three-Dimensional Attribute Modeling and Deep Mineralization Prediction of Vein 171 in Linglong Gold Field, Jiaodong Peninsula, Eastern China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript presents a 3D modeling study using Vulcan software to predict deep-seated mineralization of a quartz-vein-type gold deposit (Vein 171) in the Linglong gold field. The study focuses on grade distribution, structural control, and fault interactions.

While the work addresses a timely topic and provides a solid descriptive dataset, it lacks methodological innovation. The tools and workflow used are standard within Vulcan, with no clearly demonstrated improvements or novel approaches in modeling or analysis. A number of clarifications and technical corrections are necessary before the manuscript is suitable for publication.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

After a careful review, below is a list of queries requiring clarification, followed by specific comments for your attention:

General Observations

The manuscript includes explanations of basic statistical terms (e.g., sample variance, coefficient of variation (CV)), which may be unnecessary for the target audience of geoscientists and professionals, who are presumed to have foundational knowledge of statistics and geostatistics. While these definitions could be helpful for a broader readership, their explicit inclusion in a peer-reviewed article for specialists may be redundant.

Furthermore, more specialized terms (e.g., the mathematical expression of the log-normal distribution, Sichel’s T-estimator) are appropriately detailed for this audience. Authors are invited to reduce or remove basic definitions (and related equations, such as Equations 1 and 2) and focus more on presenting and discussing key results.

Main Comments:

Q1) Clarify Hard Data Used in Modeling

Please provide additional details on the hard data incorporated into the modeling process.

Q2) Ambiguity in Statistical Analysis of Sample Lengths (Orebody 1711)

There appears to be a contradiction in the characterization of the sample length distribution:

    Initially, it is described as a right-skewed unimodal distribution with moderate dispersion (SD = 0.159, CV = 0.158). Later, it is referred to as a 'degenerate' distribution, with 96% of samples concentrated at 1 m and near-zero standard deviation (σ ≈ 0) and coefficient of variation.

Issue:

    If 96% of samples are at 1 m, the standard deviation should be much closer to zero than 0.159 (given a mean of 1.009 m).

    A degenerate distribution typically implies zero variance, whereas the reported SD (0.159) and CV (0.158) suggest moderate dispersion, not near-zero variability. This inconsistency may misrepresent the actual spread of sample lengths.

Please clarify whether the distribution is truly 'degenerate' or simply highly concentrated, and ensure consistency in reporting dispersion metrics.

Q3) Inconsistency in High-Grade Threshold Calculation

The manuscript states:

    The log-transformed data follows a log-normal distribution with parameters: mean = 0.653, SD = 0.821.

    However, the high-grade threshold (≈ 7.31 g/t) is calculated using the formula:
    Threshold=exp⁡(0.653+1.475×0.906)

    Here, 0.906 is used as the standard deviation, conflicting with the earlier stated SD = 0.821 (according to my understanding).

Additional Issues:

    The text claims that the interval 0.653 ± 2.718 (3σ) contains 93% of log-normal values, with a right-tail probability of 3.5% (7%/2).

        For a standard normal distribution, ±3 sigma should cover ~99.7% of data, not 93%.

        A Z-value of 1.475 corresponds to the ~93rd percentile, not a tail probability of 3.5%.

    Recalculating with the correct SD (0.821) we get 6.44 g/t

    This differs from the stated threshold of 7.31 g/t.

Please clarify/resolve this discrepancy and justify the calculations.

Minor Issues

    Line 184: Add a reference for MapGIS67 software.

    Line 199:

        Justify the use of inverse distance weighting (IDW) over geostatistical methods (e.g., kriging).

        Specify the power exponent values used in IDW.

    Line 206 & Fig. 3: Clarify the term 'precision' in the context of the methodology.

Table 1: Inconsistent Formula for Sichel’s T-Coefficient

    The table lists:

        Sichel’s T-coefficient = 1.38

        Sample variance = 0.65

        Taylor series expansion (order 3 or 4) = 3

    Verify: If the correct value should be 1.95, or provide clarification.

References

All references are properly cited.

Figures

    General: Increase text size of the titles/text where necessary (e.g., Figures 2 & 3).

    Figure 3:

        Better explain Step 3, particularly the concept of 'precision integration' for complex vein-type orebodies.

    Figure 6:

        In the caption, add the meaning of the yellow curve.

    Figures 7 & 8:

        Move captions below the figures or clearly number each figure.

    Figure 19:

        Increase the text size of titles for better readability.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have revised the manuscript according to all my comments and reply to it well. I could accept it. Congratulation!

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

he revised version of the manuscript, along with your accompanying response letter, has answered most of the raised questions. The manuscript needs no further revision.

Best regards,