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峰峰矿区涌水水源紫外−可见光光谱聚类判识方法探究.

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Title: 峰峰矿区涌水水源紫外−可见光光谱聚类判识方法探究.
Alternate Title: Investigation of UV-Vis spectroscopy-based hierarchical clustering method for water inrush source identification in Fengfeng Mining Area.
Authors: 董东林1,2 ddl9266@163.com, 张恩雨1 bqt2200202001t@student.cumtb.edu.cn, 张陇强1, 傅培祺1, 王铁记3, 付晓洁3, 靳子栋3, 王 卓3, 曹 栋3
Source: Coal Science & Technology (0253-2336). Mar2026, Vol. 54 Issue 3, p277-291. 15p.
Subject Terms: *Ultraviolet-visible spectroscopy, *Hierarchical clustering (Cluster analysis), *Principal components analysis, *Water chemistry, *Coal mining, *Fisher discriminant analysis, *Karst hydrology
Abstract (English): Applying UV-Vis spectral analysis to the identification of water inrush sources in coal mines offers a rapid alternative to conventional hydrochemical methods. To address issues such as limited samples, weak model generalization, and poor geological interpretability in machine learning-based approaches, this study proposes an unsupervised identification model combining UV-Vis spectroscopy with hierarchical clustering. A total of 28 water samples from the Daqing limestone aquifer, Ordovician limestone aquifer, and roadway inrush water were collected from the Niuerzhuang, Sunzhuang, and Xin’an mines in the Fengfeng Mining Area. After denoising and principal component analysis (PCA), hierarchical clustering revealed strong spectral similarities between Daqing and Ordovician aquifers, suggesting potential hydraulic connectivity. The inrush water was primarily classified into the Ordovician aquifer group, with partial contribution from the Daqing aquifer. Compared with hydrochemical-based clustering, UV-Vis spectral data showed superior performance in distinguishing water samples with similar chemical signatures. Linear discriminant analysis (LDA) validated the classification, with an average discriminant score of 0.999 8 for the Ordovician group—higher than other aquifers—demonstrating model reliability and agreement with field observations. Furthermore, water level correlation analysis showed a strong positive relationship (maximum coefficient 0.94) and a 20 days lag between the two aquifers, supporting the existence of hydraulic connectivity. This study offers a practical and interpretable method for identifying inrush sources under data-limited conditions and contributes to understanding karst groundwater flow in North Chinatype coalfields. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 将光谱分析应用到煤矿突水水源识别中, 能够提高涌 (突) 水水源判识效率。针对部分研究区 样本有限、机器学习模型判识存在泛化性不足且分类结果地质解释性薄弱的问题, 提出了结合紫外− 可见光 (UV-Vis) 光谱分析与层次聚类的非监督式煤矿涌 (突) 水水源识别模型。以峰峰矿区牛儿庄矿、 孙庄矿和辛安矿为研究对象, 采集大青灰岩含水层、奥陶系灰岩含水层以及巷道涌水共 6 类 28 份水 样构建 UV-Vis 光谱数据集, 并进行去噪预处理和 PCA 降维。运用层次聚类结合阈值划分的方法系 统分析了各含水层水质的关联性, 发现区域内大青灰岩和奥陶系灰岩含水层水样光谱特征相似, 暗 示 2 类含水层间可能存在水力联系, 并判识巷道涌水主要来源于孙庄矿奥陶系灰岩含水层, 大青灰 岩含水层部分参与了涌水。对比水化学数据的水样分类结果, 发现 UV-Vis 光谱数据能够更快速、精 确地区分具有相似水化学特征的非同源水样。线性判别分析 (LDA) 结果表明: 涌水样本归属孙庄矿 奥陶系灰岩含水层的平均判别得分最高 (0.999 8), 大青灰岩含水层类别次之 (0.998 4), 验证了该识 别结果的可靠性, 并与工程实际情况相吻合。进一步结合水动力场时空演化分析, 揭示了大青灰岩 与奥陶系灰岩含水层水位变化存在强正相关性, 相关系数最高可达 0.94, 且出现了约 20 d 的滞后补 给特征, 证实了二者间存在水力联系。本研究可为数据稀缺条件下煤矿涌 (突) 水水源识别提供新的 技术路径, 并为华北型煤田岩溶地下水流场研究提供参考。 [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: 峰峰矿区涌水水源紫外−可见光光谱聚类判识方法探究.
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  Data: Investigation of UV-Vis spectroscopy-based hierarchical clustering method for water inrush source identification in Fengfeng Mining Area.
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  Data: <searchLink fieldCode="AR" term="%22董东林%22">董东林</searchLink><relatesTo>1,2</relatesTo><i> ddl9266@163.com</i><br /><searchLink fieldCode="AR" term="%22张恩雨%22">张恩雨</searchLink><relatesTo>1</relatesTo><i> bqt2200202001t@student.cumtb.edu.cn</i><br /><searchLink fieldCode="AR" term="%22张陇强%22">张陇强</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22傅培祺%22">傅培祺</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22王铁记%22">王铁记</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22付晓洁%22">付晓洁</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22靳子栋%22">靳子栋</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22王+卓%22">王 卓</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22曹+栋%22">曹 栋</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Coal+Science+%26+Technology+%280253-2336%29%22">Coal Science & Technology (0253-2336)</searchLink>. Mar2026, Vol. 54 Issue 3, p277-291. 15p.
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  Data: *<searchLink fieldCode="DE" term="%22Ultraviolet-visible+spectroscopy%22">Ultraviolet-visible spectroscopy</searchLink><br />*<searchLink fieldCode="DE" term="%22Hierarchical+clustering+%28Cluster+analysis%29%22">Hierarchical clustering (Cluster analysis)</searchLink><br />*<searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+chemistry%22">Water chemistry</searchLink><br />*<searchLink fieldCode="DE" term="%22Coal+mining%22">Coal mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Fisher+discriminant+analysis%22">Fisher discriminant analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Karst+hydrology%22">Karst hydrology</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: Applying UV-Vis spectral analysis to the identification of water inrush sources in coal mines offers a rapid alternative to conventional hydrochemical methods. To address issues such as limited samples, weak model generalization, and poor geological interpretability in machine learning-based approaches, this study proposes an unsupervised identification model combining UV-Vis spectroscopy with hierarchical clustering. A total of 28 water samples from the Daqing limestone aquifer, Ordovician limestone aquifer, and roadway inrush water were collected from the Niuerzhuang, Sunzhuang, and Xin’an mines in the Fengfeng Mining Area. After denoising and principal component analysis (PCA), hierarchical clustering revealed strong spectral similarities between Daqing and Ordovician aquifers, suggesting potential hydraulic connectivity. The inrush water was primarily classified into the Ordovician aquifer group, with partial contribution from the Daqing aquifer. Compared with hydrochemical-based clustering, UV-Vis spectral data showed superior performance in distinguishing water samples with similar chemical signatures. Linear discriminant analysis (LDA) validated the classification, with an average discriminant score of 0.999 8 for the Ordovician group—higher than other aquifers—demonstrating model reliability and agreement with field observations. Furthermore, water level correlation analysis showed a strong positive relationship (maximum coefficient 0.94) and a 20 days lag between the two aquifers, supporting the existence of hydraulic connectivity. This study offers a practical and interpretable method for identifying inrush sources under data-limited conditions and contributes to understanding karst groundwater flow in North Chinatype coalfields. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 将光谱分析应用到煤矿突水水源识别中, 能够提高涌 (突) 水水源判识效率。针对部分研究区 样本有限、机器学习模型判识存在泛化性不足且分类结果地质解释性薄弱的问题, 提出了结合紫外− 可见光 (UV-Vis) 光谱分析与层次聚类的非监督式煤矿涌 (突) 水水源识别模型。以峰峰矿区牛儿庄矿、 孙庄矿和辛安矿为研究对象, 采集大青灰岩含水层、奥陶系灰岩含水层以及巷道涌水共 6 类 28 份水 样构建 UV-Vis 光谱数据集, 并进行去噪预处理和 PCA 降维。运用层次聚类结合阈值划分的方法系 统分析了各含水层水质的关联性, 发现区域内大青灰岩和奥陶系灰岩含水层水样光谱特征相似, 暗 示 2 类含水层间可能存在水力联系, 并判识巷道涌水主要来源于孙庄矿奥陶系灰岩含水层, 大青灰 岩含水层部分参与了涌水。对比水化学数据的水样分类结果, 发现 UV-Vis 光谱数据能够更快速、精 确地区分具有相似水化学特征的非同源水样。线性判别分析 (LDA) 结果表明: 涌水样本归属孙庄矿 奥陶系灰岩含水层的平均判别得分最高 (0.999 8), 大青灰岩含水层类别次之 (0.998 4), 验证了该识 别结果的可靠性, 并与工程实际情况相吻合。进一步结合水动力场时空演化分析, 揭示了大青灰岩 与奥陶系灰岩含水层水位变化存在强正相关性, 相关系数最高可达 0.94, 且出现了约 20 d 的滞后补 给特征, 证实了二者间存在水力联系。本研究可为数据稀缺条件下煤矿涌 (突) 水水源识别提供新的 技术路径, 并为华北型煤田岩溶地下水流场研究提供参考。 [ABSTRACT FROM AUTHOR]
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        Value: 10.12438/cst.2025-0327
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        Text: Chinese
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        PageCount: 15
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      – SubjectFull: Ultraviolet-visible spectroscopy
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      – SubjectFull: Hierarchical clustering (Cluster analysis)
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      – SubjectFull: Water chemistry
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      – SubjectFull: Coal mining
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      – SubjectFull: Fisher discriminant analysis
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      – SubjectFull: Karst hydrology
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      – TitleFull: 峰峰矿区涌水水源紫外−可见光光谱聚类判识方法探究.
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            – D: 01
              M: 03
              Text: Mar2026
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