A Novel Method for Determining the Optimal Transition Point from Surface to Underground Exploitation of Dimension Stone

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript presents a novel and practically relevant methodology for determining the optimal transition depth from surface to underground dimension stone mining. The approach is well-structured. 

Please explicitly state how the proposed method advances beyond existing approaches (e.g., those in Refs. [7], [8], [33])—particularly regarding economic modeling, block recovery, or dynamic cost considerations.

A brief discussion on the sensitivity of the optimal transition point to key parameters (e.g., discount rate, stone price, or recovery coefficient) would strengthen the robustness of the proposed method.

The conclusion states the method is “applicable to all dimension stone deposits.” Please add a sentence noting any geological or geometric prerequisites (e.g., minimum deposit thickness, dip angle) to ensure technical accuracy.

Minor grammatical errors and typos are present (e.g., duplicated words on page 9, line 2: “the the”). A thorough proofreading or professional language editing is recommended.

Author Response

Comments 1: Please explicitly state how the proposed method advances beyond existing approaches (e.g., those in Refs. [7], [8], [33])—particularly regarding economic modeling, block recovery, or dynamic cost considerations.

Response 1:

Thank you for pointing this out. We agree with this comment. To ensure the advancement over existing methods is evident, we have clarified our methodology in the revised article. The additional section is located on page 4, lines 141 to 183. The added text is also stated below:

The dimension stone exploitation optimisation method (DS-EOM) represents a significant advancement in the modelling and evaluation of the transition from surface to underground mining for dimension stone deposits. In contrast to traditional methods designed for metal and coal deposits, which often utilize static block models, robust grade estimation, and regular deposit geometry assumptions, DS-EOM addresses the complexity and distinctive features of dimension stone deposits. These deposits are defined by heterogeneous recovery rates—where the economic value is tied to the proportion of marketable blocks rather than mineral grade—along with shallow and highly irregular geometries that make traditional block modelling inefficient and less representative. Additionally, dimension stone deposits are evaluated based on block quality and recoverable volume, not grade, necessitating a departure from grade-centric frameworks.

The DS-EOM method models the deposit as a sequence of exploitable layers instead of a detailed block-by-block approach. This layer-based representation is more flexible and better reflects the actual geological and operational conditions found in dimension stone deposits. Within this framework, blocks (block A, block B and block C) are designated for either surface or underground exploitation, and techno-economic factors are calculated for each scenario to determine the optimal transition point.

A key innovation of the DS-EOM approach lies in its economic modelling process. Unlike earlier methods that require specialized software for techno-economic calculations, DS-EOM utilizes widely available spreadsheet tools, making the process more accessible and facilitating rapid adjustments in response to market changes. Stakeholders can dynamically evaluate profitability, recovery coefficients (which represents the ratio between the volume of commercial blocks and the total volume of exploited mass, (k_i), and depends on the geological conditions within the deposit [38], [39], [40], [41], [42].), and cost structures, leading to real-time updates and greater adaptability in project planning. This capability stands in stark contrast to the rigidity of classical, software-dependent approaches, offering clear improvements in terms of flexibility and responsiveness.

Furthermore, DS-EOM prioritizes the direct calculation of economic impacts resulting from variable recovery rates and fluctuating costs. This enables a tailored determination of the optimal transition contour between surface and underground mining that accommodates the changing nature of both market and operational conditions. As a result, the method delivers a more precise, practical, and adaptable solution for identifying the best transition point, addressing the shortcomings of previous methodologies.

By integrating these conceptual and technical innovations, DS-EOM supports improved project planning and operational decision-making in dimension stone exploitation. The method not only enhances the accuracy of transition modelling but also empowers decision-makers with a tool that reflects the true complexity of dimension stone deposits, ultimately driving better economic outcomes and more sustainable exploitation strategies. Table 1 presents the principal parameters of DS-EOM in comparison with the classical transition methods.

 

Table 1. DS-EOM vs. Classical Transition Models

Parameter	DS-EOM Method	Classical Transition Methods
Deposit Representation	Layer-based, flexible modelling of shallow/irregular geometries	Static block models, best suited for massive/regular deposits
Grade/Value Modelling	Focuses on heterogeneous recovery rates and block quality/volume, not mineral grade	Relies on robust grade estimation for economic analysis
Economic Evaluation Tools	Spreadsheet-based, rapid and accessible updates; enables real-time adjustments to market changes	Specialized mining software; less flexible and slower to adapt to market changes
Recovery Rate Handling	Explicitly models spatial variability in recovery rates and block quality	Assumes homogeneous or grade-driven recovery
Geometry Adaptability	Optimized for shallow, irregular dimension stone deposits	Optimized for deep, regular metal/coal deposits
Optimal Transition Point Determination	Flexible, scenario-based, tailored to deposit specifics and economic conditions	Rigid, often based on static economic cutoffs
Responsiveness to Market/Operational Changes	Highly adaptable—allows dynamic planning and updates as conditions change	Limited adaptability—requires specialized tools and slower recalibration

 

Comments 2: A brief discussion on the sensitivity of the optimal transition point to key parameters (e.g., discount rate, stone price, or recovery coefficient) would strengthen the robustness of the proposed method.

Response 2: Thank you for pointing this out. We agree with this comment. We have added a brief discussion on the sensitivity of the optimal transition point to key parameters, as recommended, and indicated its location (line 521 to 530) in the revised manuscript. The added text is also stated below:

The optimal transition point (OTP) from surface to underground mining of dimension stone is influenced by key parameters like the discount rate, stone price, and recovery coefficient. A higher discount rate reduces the present value of future revenues, potentially delaying the transition, while a lower rate might justify an earlier transition. Fluctuations in stone prices directly impact profitability, with higher prices favouring an earlier transition and lower prices delaying it. The recovery coefficient, representing the efficiency of stone extraction, also plays a crucial role; higher recovery rates may lead to an earlier transition to underground mining, while lower rates could extend surface operations. By analysing these parameters, we can better understand the proposed method under varying conditions.

 

Comments 3: The conclusion states the method is “applicable to all dimension stone deposits.” Please add a sentence noting any geological or geometric prerequisites (e.g., minimum deposit thickness, dip angle) to ensure technical accuracy.

Response 3: Thank you for pointing this out. We strongly believe that presented methodology is adaptable to various dimension stone deposits. The exploitation blocks A, B and C can be adjusted in size so there is no need for minimum deposit thickness. Also, the deposit dip angle is also not influencing factor because if the deposit is dipping at the steep angle by using proposed methodology the OTP will be much sooner regarding the OTP for the deposit that is at angle from 5 to 15 degrees.

We have added a clarifying sentences in the revised manuscript:

line 407 to 410:

It is important to note that the dimensions of blocks A, B, and C may be adjusted according to the unique characteristics of the dimension stone deposit; however, the minimum dimension is constrained by the used mechanization.

line 671 to 681:

The DS-EOM method is specifically designed for the determination of the optimal transition point between surface and underground exploitation in dimension stone (DS) deposits. Its application is most effective in deposits where both exploitation methods are technically feasible, and where there is sufficient geological, geometric, and economic data to support detailed techno-economic analysis. The method is applicable regardless of the specific lithology or geometry of the DS deposit, provided that the deposit can be discretised into blocks for comparative evaluation. However, the accuracy and reliability of the DS-EOM approach are contingent upon the quality and resolution of input data, as well as the appropriateness of the economic parameters used. The method may be less suitable for deposits with highly irregular geometries, complex tectonic settings, or where the eco-nomic or technical feasibility of either exploitation method is fundamentally constrained.

 

Comments 4: Minor grammatical errors and typos are present (e.g., duplicated words on page 9, line 2: “the the”). A thorough proofreading or professional language editing is recommended.

Response 4: The article was proofread by a professional. 

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for submitting your manuscript. Your work offers a valuable case study and an applicable framework for evaluating the transition from surface to underground exploitation in dimension stone deposits. With strengthened methodology, clearer novelty, deeper discussion, improved figures, and language polishing, the manuscript could become suitable for publication in “Applied Sciences”.

1. At present, the DS-EOM method appears similar to existing open-pit to underground transition models. Please clarify what is new in your approach, especially in the context of dimension stone deposits. For example, DS quarries have unique characteristics (heterogeneous recovery rates, lack of grade model, shallow irregular geometry). If your model explicitly incorporates these, highlight it as an innovation. Consider adding a comparison table showing differences between your approach and classical transition frameworks.
2. The three “parts” of the DS-EOM workflow should be formalized with symbols, equations, or pseudocode.
3. Important variables (e.g., TE-u, TE-s, block codes, recovery rates) should be defined upon first appearance.
4. Please explain the basis for choosing specific recovery rates and cost parameters; providing references or calibration procedures would strengthen credibility.
5. Please add mechanistic explanations, such as: Why waste rock increases sharply for block C underground？Why surface exploitation costs escalate non-linearly with elevation？How geological dip, terrain morphology, or block position influence economic outcomes？
6. Add coordinate axes or scale bars for 3D models (Figs. 7–9).
7. Expand figure captions so that each figure can stand alone without referencing the text.
8. Consider simplifying sentence structure to improve readability.
9. Avoid subjective expressions such as “it is extremely important” and use technical descriptions instead.

Author Response

Comments 1: At present, the DS-EOM method appears similar to existing open-pit to underground transition models. Please clarify what is new in your approach, especially in the context of dimension stone deposits. For example, DS quarries have unique characteristics (heterogeneous recovery rates, lack of grade model, shallow irregular geometry). If your model explicitly incorporates these, highlight it as an innovation. Consider adding a comparison table showing differences between your approach and classical transition frameworks.

Response 1: Thank you for pointing this out. We agree with this comment. We have added a brief discussion on the sensitivity of the optimal transition point to key parameters, as recommended, and indicated its location (line 141 to 183) in the revised manuscript. The added text is also stated below:

 The dimension stone exploitation optimisation method (DS-EOM) represents a significant advancement in the modelling and evaluation of the transition from surface to underground mining for dimension stone deposits. In contrast to traditional methods designed for metal and coal deposits, which often utilize static block models, robust grade estimation, and regular deposit geometry assumptions, DS-EOM addresses the complexity and distinctive features of dimension stone deposits. These deposits are defined by heterogeneous recovery rates—where the economic value is tied to the proportion of marketable blocks rather than mineral grade—along with shallow and highly irregular geometries that make traditional block modelling inefficient and less representative. Additionally, dimension stone deposits are evaluated based on block quality and recoverable volume, not grade, necessitating a departure from grade-centric frameworks.

The DS-EOM method models the deposit as a sequence of exploitable layers instead of a detailed block-by-block approach. This layer-based representation is more flexible and better reflects the actual geological and operational conditions found in dimension stone deposits. Within this framework, blocks (block A, block B and block C) are designated for either surface or underground exploitation, and techno-economic factors are calculated for each scenario to determine the optimal transition point.

A key innovation of the DS-EOM approach lies in its economic modelling process. Unlike earlier methods that require specialized software for techno-economic calculations, DS-EOM utilizes widely available spreadsheet tools, making the process more accessible and facilitating rapid adjustments in response to market changes. Stakeholders can dynamically evaluate profitability, recovery coefficients (which represents the ratio between the volume of commercial blocks and the total volume of exploited mass, (k_i), and depends on the geological conditions within the deposit [38], [39], [40], [41], [42].), and cost structures, leading to real-time updates and greater adaptability in project planning. This capability stands in stark contrast to the rigidity of classical, software-dependent approaches, offering clear improvements in terms of flexibility and responsiveness.

Furthermore, DS-EOM prioritizes the direct calculation of economic impacts resulting from variable recovery rates and fluctuating costs. This enables a tailored determination of the optimal transition contour between surface and underground mining that accommodates the changing nature of both market and operational conditions. As a result, the method delivers a more precise, practical, and adaptable solution for identifying the best transition point, addressing the shortcomings of previous methodologies.

By integrating these conceptual and technical innovations, DS-EOM supports improved project planning and operational decision-making in dimension stone exploitation. The method not only enhances the accuracy of transition modelling but also empowers decision-makers with a tool that reflects the true complexity of dimension stone deposits, ultimately driving better economic outcomes and more sustainable exploitation strategies. Table 1 presents the principal parameters of DS-EOM in comparison with the classical transition methods.

 

Table 1. DS-EOM vs. Classical Transition Models

Parameter	DS-EOM Method	Classical Transition Methods
Deposit Representation	Layer-based, flexible modelling of shallow/irregular geometries	Static block models, best suited for massive/regular deposits
Grade/Value Modelling	Focuses on heterogeneous recovery rates and block quality/volume, not mineral grade	Relies on robust grade estimation for economic analysis
Economic Evaluation Tools	Spreadsheet-based, rapid and accessible updates; enables real-time adjustments to market changes	Specialized mining software; less flexible and slower to adapt to market changes
Recovery Rate Handling	Explicitly models spatial variability in recovery rates and block quality	Assumes homogeneous or grade-driven recovery
Geometry Adaptability	Optimized for shallow, irregular dimension stone deposits	Optimized for deep, regular metal/coal deposits
Optimal Transition Point Determination	Flexible, scenario-based, tailored to deposit specifics and economic conditions	Rigid, often based on static economic cutoffs
Responsiveness to Market/Operational Changes	Highly adaptable—allows dynamic planning and updates as conditions change	Limited adaptability—requires specialized tools and slower recalibration

 

Comments 2: The three “parts” of the DS-EOM workflow should be formalized with symbols, equations, or pseudocode.

Response 2: Thank you for your suggestion regarding the formalization of the DS-EOM workflow. The first section of the DS-EOM is conducted entirely within specialized 3D modelling software (specifically, OpenRoads Designer), and as such, it is most appropriately represented through software outputs rather than by symbols or equations. Similarly, the second section is primarily dependent on the characteristics of the specific deposit being analysed. This part also relies on 3D modelling within the specialized software to model quarries and calculate volumes, so it does not readily lend itself to symbolic or equation-based representation. However, the third section of the workflow is formalized using symbols and equations, as detailed in lines 257 to 341 of the revised article.

The algorithm was intentionally presented in this way to demonstrate that all the essential components required for the DS–EOM method are included in a single diagram. This approach highlights the comprehensive nature of the workflow, making it clear that users can access all the necessary information and procedural steps at a glance, without needing to refer to multiple sources or representations. We hope this clarifies our approach and appreciate your helpful feedback.

 

Comments 3: Important variables (e.g., TE-u, TE-s, block codes, recovery rates) should be defined upon first appearance.

3: Thank you for pointing this out. We agree with this comment. In the revised manuscript the added text is as follows:

line 198 to 202

Third section – involves determining the economic values of the quarry models, that is, defining and analysing the costs (  - costs of underground DS exploitation, €,  - costs of surface DS exploitation, €), revenues, profits, and minimum selling prices of dimension stone blocks obtained through surface and underground exploitation.

line 153 to 157

This layer-based representation is more flexible and better reflects the actual geological and operational conditions found in dimension stone deposits. Within this framework, blocks (block A, block B and block C) are designated for either surface or underground exploitation, and techno-economic factors are calculated for each scenario to determine the optimal transition point.

line 161 to 166

Stakeholders can dynamically evaluate profitability, recovery coefficients (which represents the ratio between the volume of commercial blocks and the total volume of exploited mass, (ki ), and depends on the geological conditions within the deposit [38], [39], [40], [41], [42].), and cost structures, leading to real-time updates and greater adaptability in project planning.

 

Comments 4: Please explain the basis for choosing specific recovery rates and cost parameters; providing references or calibration procedures would strengthen credibility.

Response 4: Thank you for your valuable feedback. It should be noted that the costs applied in this study were not determined specifically for the deposit in question. Instead, cost parameters were adopted from a comparable deposit to provide a realistic foundation for economic analysis. The recovery rate, however, is derived from the actual mining project data for the examined deposit, ensuring that this parameter accurately reflects site-specific conditions. Relying on established data from similar projects and the actual mining plan supports methodological integrity and avoids reliance on speculative or arbitrary values. If the proposed method is implemented in practice, all parameters—including costs—should be selected and calibrated using site-specific data and authoritative references to ensure accuracy and reliability.

In the revised manuscript the paragraph was modified to better explain techno-economic factors (line 665 to 670):

A comprehensive analysis of techno-economic factors—including recovery rates, exploitation costs, revenue generated from the sale of DS blocks, and overall profit—was conduct-ed for each block to determine the optimal transition point (OTP) from surface to under-ground quarry exploitation. When assessing these techno-economic factors, it is essential to adapt them to the specific conditions present within the deposit, as well as to the pre-vailing economic circumstances at the location where the method is to be applied.

 

Comments 5: Please add mechanistic explanations, such as: Why waste rock increases sharply for block C underground？Why surface exploitation costs escalate non-linearly with elevation？How geological dip, terrain morphology, or block position influence economic outcomes？

Response 5: Thank you for highlighting this issue. We concur with your observation. In the revised manuscript, the following text has been incorporated:

line 476 to 479

The quantity of waste rock associated with blocks B and C is exhibiting an upward trend. This increase is due to the parameters of the DS layers, which are dipping relative to the base plateau. As the DS layers dip further away from the base plateau, a greater volume of overburden must be removed to mine the productive layers.

line 566 to 577

Surface exploitation costs rise non-linearly as terrain elevation increases, due to the disproportionately greater volume and complexity of overburden that must be removed. This means that each new block mined not only adds more overburden to be removed but also requires longer haulage distances and more intensive site management, further in-creasing costs at an accelerating rate.

Key geological and geographical factors exacerbate these cost increases. A steeper geological dip can force the quarry to follow less accessible stone layers, requiring more mining and support. Challenging terrain morphology—such as rugged or uneven ground—demands extra preparation, specialised equipment, and sometimes additional infrastructure such as roads or drainage. Blocks farther from the quarry entrance mean materials must be transported over longer distances, increasing fuel and labour requirements. 

 

Comments 6: Add coordinate axes or scale bars for 3D models (Figs. 7–9).

Response 6: Thank you for pointing this out. We agree with this comment. In the revised manuscript scale was added to the figures (Figure 4 to Figure 13).

 

Comments 7: Expand figure captions so that each figure can stand alone without referencing the text.

Response 7: Thank you for pointing this out. We agree with this comment. In the revised manuscript the figures are now standing alone (Figure 5 to Figure 13).

 

Comments 8: Consider simplifying sentence structure to improve readability.

Response 8: Thank you for pointing this out. We agree with this comment. In the revised manuscript the text was altered as follows:

line 13 to 15:

The decision regarding the mining method—surface, underground, or combined—is made before mining operations commence. This occurs during preliminary, pre-investment, and investment studies.

line 21 to 22:

The position of the OTP from surface to underground mining of dimension stone, is not a constant value; it changes over time and space according to techno-economic factors.

line 28 to 39:

The selection of method—surface, underground, or combined—depends on factors such as deposit size, shape, and orientation; rock mass properties; mining capacity and losses; recovery rate; overburden volume; machinery capabilities; environmental and social aspects; climate; discount rate; investment costs; mining costs; depreciation; and profit [6], [7], [8], [9]. The primary objective is to find the most favourable mine contour at which exploitation is economically optimal (profitable), while also ensuring that safety and environmental protection requirements are met.

Underground mining offers several advantages over surface mining, including land reclamation, better control of deposit quality, economic value, operational flexibility, im-proved safety, and lower environmental impact—which is now a key factor [10], [11], [12], [13].

 line 245 to 247: 

The design process was undertaken in a three-dimensional environment. This approach immediately generates models of the final quarry contours, enabling calculation of exploited volumes of DS and waste rock for both surface and underground quarries.

 

Comments 9: Avoid subjective expressions such as “it is extremely important” and use technical descriptions instead.

Response 9: Thank you for pointing this out. We agree with this comment. In the revised manuscript the text was altered as follows:

line 39 to 41

Accurately determining the optimal exploitation contour for surface, underground, or combined mining is essential for minimising financial investment during mine development [14], [15], [16].

Reviewer 3 Report

Comments and Suggestions for Authors

In this paper, a novel method for determining the optimal transition point from surface to underground exploitation of dimension stone deposits is presented, based on the analysis of techno-economic factors. The optimal transition point represents the economically optimal contour for surface, underground, or combined exploitation of the dimension stone deposit.

1. Although the introduction part points out the insufficiency of the research on the transition point of dimension stone (DS) deposits and metal deposits, it lacks a systematic analysis and summary of the advantages and disadvantages of the existing transition point methods for metal deposits (such as random models and economic block models).
2. The mining cost parameters cited in the economic analysis do not specify whether they have been adjusted according to the specific geological conditions and mining equipment of the deposit, nor have they been verified for their applicability to the “Crvene stijene” deposit. Please provide necessary explanations to enhance the rationality of the cost parameters.
3. Latest research work related with this topic can be referred. Study on the energy evolution mechanism and fractal characteristics of coal failure under dynamic loading. Diffusion evolution rules of grouting slurry in mining-induced cracks in overlying strata. Research on overburden structural characteristics and support adaptability in cooperative mining of sectional coal pillar and bottom coal seam.
4. The interpretation of the results lacks depth. The reasons for the exponential growth of surface mining costs need to be further explored. The optimal transition point is determined between B and C, but the specific depth corresponding to the transition point is not clearly stated, making it impossible to compare with existing transition point studies.
5. The conclusion part does not clearly define the application scope and limitations of the DS-EOM method. It only vaguely mentions that it can be applied to other deposits, lacking a clear summary of the method’s application scope.

Author Response

Comments 1: Although the introduction part points out the insufficiency of the research on the transition point of dimension stone (DS) deposits and metal deposits, it lacks a systematic analysis and summary of the advantages and disadvantages of the existing transition point methods for metal deposits (such as random models and economic block models).

Response 1: Thank you for pointing this out. We agree with this comment. We have added a brief discussion on the sensitivity of the optimal transition point to key parameters, as recommended, and indicated its location (line 142 to 184) in the revised manuscript. The added text is also stated below:

 The dimension stone exploitation optimisation method (DS-EOM) represents a significant advancement in the modelling and evaluation of the transition from surface to underground mining for dimension stone deposits. In contrast to traditional methods designed for metal and coal deposits, which often utilize static block models, robust grade estimation, and regular deposit geometry assumptions, DS-EOM addresses the complexity and distinctive features of dimension stone deposits. These deposits are defined by heterogeneous recovery rates—where the economic value is tied to the proportion of marketable blocks rather than mineral grade—along with shallow and highly irregular geometries that make traditional block modelling inefficient and less representative. Additionally, dimension stone deposits are evaluated based on block quality and recoverable volume, not grade, necessitating a departure from grade-centric frameworks.

The DS-EOM method models the deposit as a sequence of exploitable layers instead of a detailed block-by-block approach. This layer-based representation is more flexible and better reflects the actual geological and operational conditions found in dimension stone deposits. Within this framework, blocks (block A, block B and block C) are designated for either surface or underground exploitation, and techno-economic factors are calculated for each scenario to determine the optimal transition point.

A key innovation of the DS-EOM approach lies in its economic modelling process. Unlike earlier methods that require specialized software for techno-economic calculations, DS-EOM utilizes widely available spreadsheet tools, making the process more accessible and facilitating rapid adjustments in response to market changes. Stakeholders can dynamically evaluate profitability, recovery coefficients (which represents the ratio between the volume of commercial blocks and the total volume of exploited mass, (k_i), and depends on the geological conditions within the deposit [38], [39], [40], [41], [42].), and cost structures, leading to real-time updates and greater adaptability in project planning. This capability stands in stark contrast to the rigidity of classical, software-dependent approaches, offering clear improvements in terms of flexibility and responsiveness.

Furthermore, DS-EOM prioritizes the direct calculation of economic impacts resulting from variable recovery rates and fluctuating costs. This enables a tailored determination of the optimal transition contour between surface and underground mining that accommodates the changing nature of both market and operational conditions. As a result, the method delivers a more precise, practical, and adaptable solution for identifying the best transition point, addressing the shortcomings of previous methodologies.

By integrating these conceptual and technical innovations, DS-EOM supports improved project planning and operational decision-making in dimension stone exploitation. The method not only enhances the accuracy of transition modelling but also empowers decision-makers with a tool that reflects the true complexity of dimension stone deposits, ultimately driving better economic outcomes and more sustainable exploitation strategies. Table 1 presents the principal parameters of DS-EOM in comparison with the classical transition methods.

 

Table 1. DS-EOM vs. Classical Transition Models

Parameter	DS-EOM Method	Classical Transition Methods
Deposit Representation	Layer-based, flexible modelling of shallow/irregular geometries	Static block models, best suited for massive/regular deposits
Grade/Value Modelling	Focuses on heterogeneous recovery rates and block quality/volume, not mineral grade	Relies on robust grade estimation for economic analysis
Economic Evaluation Tools	Spreadsheet-based, rapid and accessible updates; enables real-time adjustments to market changes	Specialized mining software; less flexible and slower to adapt to market changes
Recovery Rate Handling	Explicitly models spatial variability in recovery rates and block quality	Assumes homogeneous or grade-driven recovery
Geometry Adaptability	Optimized for shallow, irregular dimension stone deposits	Optimized for deep, regular metal/coal deposits
Optimal Transition Point Determination	Flexible, scenario-based, tailored to deposit specifics and economic conditions	Rigid, often based on static economic cutoffs
Responsiveness to Market/Operational Changes	Highly adaptable—allows dynamic planning and updates as conditions change	Limited adaptability—requires specialized tools and slower recalibration

 

Comments 2: The mining cost parameters cited in the economic analysis do not specify whether they have been adjusted according to the specific geological conditions and mining equipment of the deposit, nor have they been verified for their applicability to the “Crvene stijene” deposit. Please provide necessary explanations to enhance the rationality of the cost parameters.

Response 2: Thank you for your valuable feedback. It should be noted that the costs applied in this study were not determined specifically for the deposit in question. Instead, cost parameters were adopted from a comparable deposit to provide a realistic foundation for economic analysis. The recovery rate, however, is derived from the actual mining project data for the examined deposit, ensuring that this parameter accurately reflects site-specific conditions. Relying on established data from similar projects and the actual mining plan supports methodological integrity and avoids reliance on speculative or arbitrary values. If the proposed method is implemented in practice, all parameters—including costs—should be selected and calibrated using site-specific data and authoritative references to ensure accuracy and reliability.

In the revised manuscript the paragraph was modified to better explain techno-economic factors (line 662 to 667):

A comprehensive analysis of techno-economic factors—including recovery rates, exploitation costs, revenue generated from the sale of DS blocks, and overall profit—was conduct-ed for each block to determine the optimal transition point (OTP) from surface to under-ground quarry exploitation. When assessing these techno-economic factors, it is essential to adapt them to the specific conditions present within the deposit, as well as to the pre-vailing economic circumstances at the location where the method is to be applied.

 

Comments 3: Latest research work related with this topic can be referred. Study on the energy evolution mechanism and fractal characteristics of coal failure under dynamic loading. Diffusion evolution rules of grouting slurry in mining-induced cracks in overlying strata. Research on overburden structural characteristics and support adaptability in cooperative mining of sectional coal pillar and bottom coal seam.

Response 3: Thank you for your valuable feedback regarding the suggested references. While the cited works indeed offer relevance in the field of underground mining, they pertain to a separate area of research from the present study. Specifically, these references focus on aspects of underground mining operations that do not address the methodology for transitioning from surface to underground mining of dimension stone deposits. As such, they fall outside the intended scope of this research, which is centred on the transition methodology for mining dimension stone. Accordingly, the references provided cannot be directly utilised in the context of this study.

 

Comments 4: The interpretation of the results lacks depth. The reasons for the exponential growth of surface mining costs need to be further explored. The optimal transition point is determined between B and C, but the specific depth corresponding to the transition point is not clearly stated, making it impossible to compare with existing transition point studies.

Response 4: Thank you for pointing this out. We agree with this comment. In the revised manuscript the added text is as follows:

line 556 to 577

Surface exploitation costs rise non-linearly as terrain elevation increases, due to the disproportionately greater volume and complexity of overburden that must be removed. This means that each new block mined not only adds more overburden to be removed but also requires longer haulage distances and more intensive site management, further in-creasing costs at an accelerating rate.

Key geological and geographical factors exacerbate these cost increases. A steeper geological dip can force the quarry to follow less accessible stone layers, requiring more mining and support. Challenging terrain morphology—such as rugged or uneven ground—demands extra preparation, specialised equipment, and sometimes additional infrastructure such as roads or drainage. Blocks farther from the quarry entrance mean materials must be transported over longer distances, increasing fuel and labour requirements.

line 645 to 650

The optimal transition contour or optimal transition point (OTP) is situated between blocks B and C. However, determining the precise location of the OTP requires a more granular approach. This can be achieved by introducing additional blocks within this interval, which will allow for a more precise determination of the OTP. By refining the block segmentation between B and C, it becomes possible to accurately identify the specific location where the shift from surface to underground exploitation should occur. It should be noted that the OTP does not primarily define the optimal depth; rather, it defines the spatial and temporal position for shifting from open pit to underground DS deposit exploitation.

 

Comments 5: The conclusion part does not clearly define the application scope and limitations of the DS-EOM method. It only vaguely mentions that it can be applied to other deposits, lacking a clear summary of the method’s application scope.

Response 5: Thank you for pointing this out. We agree with this comment. In the revised manuscript the added text is as follows:

line 671 to 688

The DS-EOM method is specifically designed for the determination of the optimal transition point between surface and underground exploitation in dimension stone (DS) deposits. Its application is most effective in deposits where both exploitation methods are technically feasible, and where there is sufficient geological, geometric, and economic data to support detailed techno-economic analysis. The method is applicable regardless of the specific lithology or geometry of the DS deposit, provided that the deposit can be discretised into blocks for comparative evaluation. However, the accuracy and reliability of the DS-EOM approach are contingent upon the quality and resolution of input data, as well as the appropriateness of the economic parameters used. The method may be less suitable for deposits with highly irregular geometries, complex tectonic settings, or where the eco-nomic or technical feasibility of either exploitation method is fundamentally constrained.

Furthermore, as an opportunity for further research, development, and application of this method, a software solution could be created that would enable the new methodology to be efficiently, quickly, simply, and accurately applied depending on deposit conditions. Additionally, the accuracy of the method can be further improved by introducing additional blocks in the analysis. This refinement would allow for a more precise determination of the optimal transition point (OTP), ensuring that the shift from surface to under-ground exploitation is identified with greater exactness.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Accept