基于半监督学习的煤层钻孔预抽瓦斯状态评估方法研究
State Evaluation Method for Borehole Pre-Extraction of Coalbed Methane Based on Semi-Supervised Learning
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摘要: 在煤矿安全生产中，煤层瓦斯的有效抽采对于资源利用和灾害预防至关重要。传统的瓦斯抽采效率评估方法多依赖于现场取样检测，但这种方法存在时间掌握不准确、人力物力浪费以及无法实时反馈预抽效果等局限性。为了克服这些问题，本研究提出了一种基于数据驱动的半监督学习方法，用于精细化科学评估煤层预抽钻孔的抽采状态。首先对数据进行预处理，选取评价指标，并采用层次分析法（AHP）和模糊评价法（FEM）相结合的方式赋予指标权重。基于AHP-FEM提出了半监督学习的高斯混合模型（GMM）和K-Means算法的混合优化模型。该方法有以下亮点：（1）高效性：结合少量标定数据，聚类相似的抽采数据，实现了对大量单一钻孔当日抽采数据的评估。（2）科学性：通过数据驱动的方式，减少了人为主观因素的影响，综合考虑了钻孔的时序特征和煤层中瓦斯分布的不均匀性，挖掘数据的内在规律，提高评价的客观性和准确性。（3）安全性：通过动态监测和反馈机制，及时发现和解决问题，优化瓦斯预抽效果，有助于预防瓦斯事故，提升煤矿安全生产水平。推动煤层预抽的高效智能管理。Abstract: In coal mine production safety, effective extraction of coalbed methane (CBM) is essential for resource utilization and disaster prevention. Traditional CBM extraction evaluation methods, relying on on-site sampling and testing, suffer from limitations such as inaccurate timing, wasted resources, and lack of real-time feedback. This study proposes a data-driven semi-supervised learning method for refined evaluation of pre-drilled CBM boreholes. The approach includes data preprocessing, selection of evaluation indicators, and the integration of Analytic Hierarchy Process (AHP) with Fuzzy Evaluation Method (FEM) for weight assignment. A semi-supervised learning model combining Gaussian Mixture Model (GMM) and K-Means clustering is developed. The method offers several advantages: (1) it integrates a small amount of calibration data with clustering similar extraction data to evaluate large quantities of daily extraction data from individual boreholes; (2) it reduces human subjective influence, comprehensively considers temporal characteristics of boreholes and heterogeneity of CBM distribution in coal seams, and uncovers intrinsic data patterns, thereby improving the objectivity and accuracy of the evaluation; (3) it includes dynamic monitoring and feedback mechanisms that allow for timely identification and resolution of issues, optimizing gas pre-extraction effects and enhancing the safety level of coal mine production.
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