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Study on Prediction Model of Tailings Organic Matter Content Based on Hyperspectral Technology
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Zijing WU1, Haihong GU1, 2, 3, 4, 5, Shaoping CHENG1, Zheng ZHOU6, Yusong REN1, Jiali SHAO1, Yanjun AI1, Guie TIAN1, 2, 3
Mining Research and Development | 2026, 46(1) : 186 - 194
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Mining Research and Development | 2026, 46(1): 186-194
Study on Prediction Model of Tailings Organic Matter Content Based on Hyperspectral Technology
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Zijing WU1, Haihong GU1, 2, 3, 4, 5, Shaoping CHENG1, Zheng ZHOU6, Yusong REN1, Jiali SHAO1, Yanjun AI1, Guie TIAN1, 2, 3
Affiliations
  • 1.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • 2.Hebei Provincial Key Laboratory of Mining Development and Safety Technology, Tangshan, Hebei 063210, China
  • 3.Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan, Hebei 063210, China
  • 4.Collaborative Innovation Center for Green Development and Ecological Restoration of Mineral Resources, Tangshan, Hebei 063210, China
  • 5.Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan, Hebei 063210, China
  • 6.College of Mechanical Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
Published: 2026-01-25
Outline
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Over-exploitation of mineral resources aggravates the ecological environmental pressure in mining areas. As solid wastes containing potential heavy metals, tailings cause damage to soil organic matter (SOM) through stockpiling, which has become a key issue in ecological restoration. The SOM monitoring system of tailings was established based on hyperspectral remote sensing technology. Taking a mining area in Tangshan, Hebei Province as the research object, vegetation canopy hyperspectral data were obtained using an ASD ground spectrometer and GF-5 satellite, and tailings samples at the corresponding points were collected for physicochemical property analysis. After screening sensitive bands with the Boruta algorithm, four types of models such as multiple stepwise linear regression (SMLR), partial least squares regression (PLSR), random forest (RF) and extreme gradient boosting (XGBoost) were constructed and verified for accuracy. The results show that the XGBoost model has the best prediction effect on the ground ASD spectral data (R2=0.92, RMSE=0.37), which is suitable for fine monitoring. The GF-5 satellite data perform best with the XGBoost model (R2=0.73, RMSE=0.53), which meets the needs of large-scale rapid assessment. The study confirms that ground-space multi-scale SOM monitoring can be realized through the collaborative optimization of spectral mathematical transformations and linear and nonlinear models, with efficiency significantly enhanced compared with traditional detection methods. The research results can provide a scientific basis for ecological restoration of mining areas, promoting environmental management of mining area and the sustainable development of mining industry.

Tailings organic matter  /  Hyperspectral technology  /  Tailings monitoring  /  Prediction model
吴梓敬, 谷海红, 成少平, 周征, 任宇松, 邵佳丽, 艾艳君, 田桂娥. 基于高光谱的尾矿有机质含量估测模型研究. 矿业研究与开发, 2026 , 46 (1) : 186 -194 .
Zijing WU, Haihong GU, Shaoping CHENG, Zheng ZHOU, Yusong REN, Jiali SHAO, Yanjun AI, Guie TIAN. Study on Prediction Model of Tailings Organic Matter Content Based on Hyperspectral Technology[J]. Mining Research and Development, 2026 , 46 (1) : 186 -194 .
Year 2026 volume 46 Issue 1
50
10
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Article Info
  • Receive Date:2025-02-05
  • Online Date:2026-10-29
  • Published:2026-01-25
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  • Received:2025-02-05
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Affiliations
    1.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
    2.Hebei Provincial Key Laboratory of Mining Development and Safety Technology, Tangshan, Hebei 063210, China
    3.Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan, Hebei 063210, China
    4.Collaborative Innovation Center for Green Development and Ecological Restoration of Mineral Resources, Tangshan, Hebei 063210, China
    5.Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan, Hebei 063210, China
    6.College of Mechanical Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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