Key Takeaways & Executive Findings
- •• Proposes an adaptive window fractal analysis method for gas concentration data, improving local feature detection over fixed window methods. • Integrates box-counting dimension and variation metrics to establish a cross-scale dynamic warning model for gas outbursts. • Achieves dynamic threshold partitioning using membership functions and the 3r principle, enabling graded classification of the MGD index. • Validated at Shoushan #1 Coal Mine, achieving 86.9% warning accuracy and demonstrating enhanced fluctuation characteristics.
Abstract
To address the issues of single warning indicators, fixed thresholds, and insufficient adaptability in coal and gas outburst early warning models, this study proposes a dynamic early warning model for gas outbursts based on adaptive fractal dimension characterization. By analyzing the nonlinear characteristics of gas concentration data, an adaptive window fractal analysis method is introduced. Combined with box-counting dimension and variation of box dimension metrics, a cross-scale dynamic warning model for disaster prevention is established. The implementation involves three key phases: First, wavelet denoising and interpolation methods are employed for raw data preprocessing, followed by validation of fractal characteristics. Second, an adaptive window cross-scale fractal dimension method is proposed to calculate the box-counting dimension of gas concentration, enabling effective capture of multi-scale complex features. Finally, dynamic threshold partitioning is achieved through membership functions and the 3r principle, establishing a graded classification standard for the mine gas disaster (MGD) index. Validated through engineering applications at Shoushan #1 Coal Mine in Henan Province, the results demonstrate that the adaptive window fractal dimension curve exhibits significantly enhanced fluctuation characteristics compared to fixed window methods, with local feature detection capability improved and warning accuracy reaching 86.9%. The research reveals that this model effectively resolves the limitations of traditional methods in capturing local features and dependency on subjective thresholds through multi-indicator fusion and threshold optimization, providing both theoretical foundation and practical tool for coal mine gas outburst early warning.
1. Introduction
Gas outburst, a term describing the sudden ejection of gas in coal mines, represents an extremely complex dynamic disaster during underground mining operations. It is recognized as one of the most critical threats to safety in coal mining practices [1–3]. Outbursts result in the rapid and massive release of gas into confined working spaces, causing severe damage to roadways, roofs, and surrounding equipment. Furthermore, they may trigger secondary disasters such as fires and gas explosions, posing grave risks to the lives of workers in affected areas [4–6].
Since the first documented incident of this nature, over 40000 coal mine safety accidents related to gas outbursts have been recorded globally [6,7]. Notably, as mining depths increase, the abruptness and destructive intensity of outbursts continue to escalate [8]. The intricate and irregular evolution of outbursts complicates the characterization of their catastrophic progression, thereby hindering the precise calculation of early warning thresholds. Consequently, advancing research on early-stage risk characterization and early warning is of paramount importance.
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Jie Chen, Wenhao Shi, Yichao Rui, Junsheng Du, Xiaokang Pan, Xiang Peng, Xusheng Zhao, Qingfeng Wang, Deping Guo, Yulin Zou, Dafa Yin, Yuanbin Luo (2025). Research on a dynamic early warning model for gas outbursts using adaptive fractal dimension characterization. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.07.004
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a dynamic early warning model for gas outbursts using adaptive fractal dimension characterization, which addresses limitations of traditional methods by improving local feature detection and reducing subjective threshold dependency.
How does the adaptive fractal method work?
The adaptive window fractal analysis method calculates the box-counting dimension of gas concentration data using an adaptive window, enabling capture of multi-scale complex features and enhancing fluctuation characteristics compared to fixed window methods.
What is the warning accuracy of the proposed model?
Validated at Shoushan #1 Coal Mine, the model achieved a warning accuracy of 86.9%.
What are the key phases of the model implementation?
The implementation involves three phases: data preprocessing (wavelet denoising and interpolation), adaptive window cross-scale fractal dimension calculation, and dynamic threshold partitioning using membership functions and the 3r principle.
How does the model handle threshold setting?
Dynamic threshold partitioning is achieved through membership functions and the 3r principle, establishing a graded classification standard for the mine gas disaster (MGD) index, thus avoiding fixed thresholds.
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