Articles | Volume 16, issue 11
https://doi.org/10.5194/se-16-1269-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
A simplified relationship between the zero-percolation threshold and fracture set properties
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- Final revised paper (published on 03 Nov 2025)
- Preprint (discussion started on 17 Jun 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2025-2440', Anonymous Referee #1, 23 Jun 2025
- AC3: '回复RC1', Shaoqun Dong, 16 Aug 2025
- RC2: 'Comment on egusphere-2025-2440', Anonymous Referee #2, 16 Jul 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Shaoqun Dong on behalf of the Authors (16 Aug 2025)
Author's response
Author's tracked changes
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ED: Referee Nomination & Report Request started (19 Aug 2025) by Jessica McBeck
RR by Anonymous Referee #2 (30 Aug 2025)
RR by Lei Gong (01 Sep 2025)
ED: Publish as is (01 Sep 2025) by Jessica McBeck
ED: Publish as is (05 Sep 2025) by Florian Fusseis (Executive editor)
AR by Shaoqun Dong on behalf of the Authors (15 Sep 2025)
Manuscript
This paper presents a systematic investigation into the derivation of simplified, direct relationships between the zero-percolation threshold (p0) and key fracture network parameters in discrete fracture networks (DFNs). Through extensive numerical experiments based on Monte Carlo simulations and rigorous non-linear multivariate regression, the authors succeed in constructing functional expressions that relate p0 to fracture number, orientation concentration (κ), and angular deviation (Δ), considering both exponential and lognormal distributions for fracture length. The derived equations are validated against numerical simulations and analytical benchmarks, demonstrating high predictive accuracy and robustness across a range of DFN configurations and spatial scales. It is clearly structured, methodologically sound, and contributes meaningfully to the fracture network modeling community, particularly in contexts where probabilistic guarantees are essential, such as nuclear waste repository site selection and unconventional reservoir modeling. Nonetheless, a few minor issues merit revision to further improve clarity, rigor, and presentation. A minor revision is suggested.
1.Several variables and equations are introduced early (e.g., μ, κ, Δ, p₀, L̅ₜ) without immediate intuitive explanation. Consider including a summarized table of symbols and parameters, especially for readers unfamiliar with von Mises distribution or DFN modeling conventions.
2.Machine learning offers a promising approach for capturing the complex relationship between fracture network properties and percolation behavior. The analytical formulation proposed in this work provides a strong foundation for integrating such physical insights into physics-informed machine learning frameworks. It is therefore recommended that the authors include a brief discussion on potential future directions, particularly the combination of their derived equations with emerging machine learning techniques, such as Kolmogorov–Arnold Networks (KAN), to enhance model generalizability and predictive capability.
3.Please ensure all citations are formatted uniformly (e.g., "Yi, Taverghi, 2009" vs. "Yi & Taverghi, 2009").
4. Equations are referenced inconsistently. A consistent format throughout will aid readability.
5. While the paper effectively presents a simplified relationship, a brief yet dedicated discussion on the inherent limitations of this simplification would further enhance the manuscript's academic rigor. Furthermore, outlining potential avenues for future research that could expand upon this simplified framework would significantly add to the paper's impact and forward-looking perspective."