Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model

Authors

ZHAO Yuxing, School of Environmental Studies, China University of Geosciences, Wuhan 430074, ChinaFollow
LI Xiangwen, College of Mining Engineering, Heilongjiang University of Science and Technology, Harbin 150028, ChinaFollow

Abstract

Objective Accurately predicting water yield of mine before mining can provide directive guidance for preventing potential water hazards and ensuring safe production. Methods To enhance the prediction accuracy and stability of water yield of open-pit metal mines, for which atmospheric precipitation acts as the primary recharge source of water, this study developed a prediction model that coupled a bidirectional recurrent neural network (Bi-RNN) and the Groundwater Modeling System (GMS) software. Specifically, based on historical forecasted precipitation data provided by the Global Forecast System (GFS), the fluctuation pattern of differences between predicted forecasted and actual precipitation was analyzed. After being corrected using the Bi-RNN, the forecasted precipitation data were input into GMS for prediction. The coupling model was employed to predict water yield of mine in the northern and southern mining areas in the study area. Concurrently, the water yield of mine in the mining areas was also predicted using both the traditional large diameter well method and the recharge modulus large diameter well method. Finally, the prediction results based on the three methods were compared. Results and Conclusions The results indicate that the coupling model, the traditional large diameter well method, and the recharge modulus large diameter well method yielded water yield of mine of 294 m³/d, 276.651 to 940.613 m³/d, and 287.241 m³/d, respectively for the northern mining area and 1160 m³/d, 3330.107 to 5090.944 m³/d, and 1108.575 m³/d, respectively for the northern mining areas. These results suggest that the proposed coupling model, a prediction method combining multiple data sources, has achieved certain results and enjoys certain advantages in predicting water yield of mine. This model provides a new philosophy and technical support for predicting water yield of mine, exhibiting high theoretical value and great potential for practical application.

Keywords

open-pit mine, predicted water yield of mine, bidirectional recurrent neural network (Bi-RNN), global forecast system (GFS), groundwater modeling system (GMS), deep learning

DOI

10.12363/issn.1001-1986.24.07.0489

Recommended Citation

Z L. (2024) "Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model," Coal Geology & Exploration: Vol. 52: Iss. 12, Article 15.
DOI: 10.12363/issn.1001-1986.24.07.0489
Available at: https://cge.researchcommons.org/journal/vol52/iss12/15

Reference

[1] 蔡长发，何辉祥，李爱华，等. 广东省廉江大垌矿区水文地质特征及矿坑涌水量预测[J]. 长春工程学院学报(自然科学版)，2021，22(3)：60−63.

CAI Changfa，HE Huixiang，LI Aihua，et al. The hydrogeological characteristics and water inflow prediction of Datong mining area Guangdong Province[J]. Journal of Changchun Institute of Technology (Natural Sciences Edition)，2021，22(3)：60−63.

[2] 余国锋，袁亮，任波，等. 底板突水灾害大数据预测预警平台[J]. 煤炭学报，2021，46(11)：3502−3514.

YU Guofeng，YUAN Liang，REN Bo，et al. Big data prediction and early warning platform for floor water inrush disaster[J]. Journal of China Coal Society，2021，46(11)：3502−3514.

[3] 张博成，孙常民. 水文地质极复杂型矿井盘区涌水量预测[J]. 中国煤炭，2023，49(增刊2)：107−112.

ZHANG Bocheng，SUN Changmin. Study on water inflow prediction of panel in coal mine with extremely complicated hydrogeology[J]. China Coal，2023，49(Sup.2)：107−112.

[4] 刘兴雷，吴顺川，韩龙强，等. 露天矿地下涌水模拟与安全防治研究[J]. 矿业研究与开发，2024，44(2)：151−156.

LIU Xinglei，WU Shunchuan，HAN Longqiang，et al. Study on simulation and safety prevention of underground water inflow in open-pit mine[J]. Mining Research and Development，2024，44(2)：151−156.

[5] 吴兵，娄鹏，王超. 基于Q-S曲线法预测工作面涌水量[J]. 矿业安全与环保，2014，41(6)：48−52.

WU Bing，LOU Peng，WANG Chao. Prediction of mine water inflow in working face based on Q-S curve method[J]. Mining Safety & Environmental Protection，2014，41(6)：48−52.

[6] 樊发旺，郭爱江，芦震，等. 基于生产因素相关性分析的矿井涌水量预测[J]. 陕西煤炭，2024，43(2)：82−85.

FAN Fawang，GUO Aijiang，LU Zhen，et al. Prediction of mine water inflow based on correlation analysis of production factors[J]. Shaanxi Coal，2024，43(2)：82−85.

[7] 钱学溥，张明燕，于义强，等. 补给模数大井法计算矿坑涌水量[J]. 工程勘察，2017，45(11)：7−12.

QIAN Xuepu，ZHANG Mingyan，YU Yiqiang，et al. Calculating of mine discharge by using recharge modulus large-well method[J]. Geotechnical Investigation & Surveying，2017，45(11)：7−12.

[8] 钱学溥，张明燕，于义强，等. 对“补给模数大井法计算矿坑涌水量” 商榷的答辩[J]. 工程勘察，2018，46(7)：71−78.

QIAN Xuepu，ZHANG Mingyan，YU Yiqiang，et al. Reply to the discussion for “Calculating of mine discharge by using recharge modulus large-well method”[J]. Geotechnical Investigation & Surveying，2018，46(7)：71−78.

[9] 王颖，刘郑秋，李成帅，等. 基于ARIMA-GM模型的矿井涌水量预测[J]. 煤炭技术，2024，43(9)：154−157.

WANG Ying，LIU Zhengqiu，LI Chengshuai，et al. Mine water inflow prediction based on ARIMA-GM model[J]. Coal Technology，2024，43(9)：154−157.

[10] 熊鹏，谢永生，韩冬，等. 基于Visual Modflow的刚果(金)迪兹瓦露天矿地下涌水量预测[J]. 科学技术与工程，2022，22(28)：12324−12330.

XIONG Peng，XIE Yongsheng，HAN Dong，et al. Prediction of underground water inflow in Deziwa open-pit mine in the Democratic Republic of Congo (DRC) based on Visual Modflow[J]. Science Technology and Engineering，2022，22(28)：12324−12330.

[11] 杨磊，雷方超，侯恩科，等. 含水层富水性分区及工作面疏放水后涌水量分段预测[J]. 煤田地质与勘探，2023，51(10)：114−123.

YANG Lei，LEI Fangchao，HOU Enke，et al. Zoned prediction of water inflow after dewatering of working face based on water richness zoning of aquifer[J]. Coal Geology & Exploration，2023，51(10)：114−123.

[12] 王飞，荣统瑞，侯恩科，等. 基于VMD-BiLSTM组合模型的矿井涌水量时间序列预测方法研究[J]. 矿业研究与开发，2024，44(3)：143−151.

WANG Fei，RONG Tongrui，HOU Enke，et al. Research on time series prediction method of mine water inflow based on VMD-BiLSTM combined model[J]. Mining Research and Development，2024，44(3)：143−151.

[13] 丁莹莹，尹尚先，连会青，等. 基于SSA-CG-Attention模型的多因素采煤工作面涌水量预测[J]. 煤田地质与勘探，2024，52(4)：111−119.

DING Yingying，YIN Shangxian，LIAN Huiqing，et al. Prediction of mine water inflow along mining faces using the SSA-CG-Attention multifactor model[J]. Coal Geology & Exploration，2024，52(4)：111−119.

[14] 侯恩科，徐林啸，荣统瑞. 彬长大佛寺矿井涌水量时序预测[J]. 西安科技大学学报，2024，44(3)：490−500.

HOU Enke，XU Linxiao，RONG Tongrui. Time series prediction of mine water inflow from Binchang Dafosi mine[J]. Journal of Xi’an University of Science and Technology，2024，44(3)：490−500.

[15] 闫和平，李文平，段中会，等. 黄陇煤田典型特厚煤层综放开采涌水机理与导水裂隙带发育规律[J]. 煤田地质与勘探，2024，52(5)：129−138.

YAN Heping，LI Wenping，DUAN Zhonghui，et al. Water inrush mechanism and water-conducting fractured zone’ developmental patterns of a typical ultra-thick coal seam in the Huanglong Coalfield during fully mechanized mining[J]. Coal Geology & Exploration，2024，52(5)：129−138.

[16] 范明星，任高峰，吴文博，等. 基于降雨量数据的程潮铁矿涌水量时序性预测模型[J]. 金属矿山，2024(6)：212−219.

FAN Mingxing，REN Gaofeng，WU Wenbo，et al. Temporal prediction model of water inflow in Chengchao iron mine based on rainfall data[J]. Metal Mine，2024(6)：212−219.

[17] 赵雷，侯克鹏，者亚雷，等. 露天大水矿床开采渗流场及帷幕防治水模拟研究[J]. 现代矿业，2023，39(7)：63−67.

ZHAO Lei，HOU Kepeng，ZHE Yalei，et al. Simulation of seepage field and curtain water control in open-pit heavy water deposit mining[J]. Modern Mining，2023，39(7)：63−67.

[18] 王献忠，刘洪利，刘忠田，等. 黑龙江省砂宝斯金矿床矿石矿物与金矿物特征研究[J]. 矿物岩石地球化学通报，2015，34(1)：101−109.

WANG Xianzhong，LIU Hongli，LIU Zhongtian，et al. Characteristics of ore minerals and gold-bearing minerals in the Shabaosi gold deposit，Heilongjiang Province[J]. Bulletin of Mineralogy，Petrology and Geochemistry，2015，34(1)：101−109.

[19] 赵炳新，宋丙剑，周殿宇，等. 黑龙江省漠河县砂宝斯金矿地质特征及成矿规律浅析[J]. 黄金科学技术，2007，15(2)：20−25.

ZHAO Bingxin，SONG Bingjian，ZHOU Dianyu，et al. Geological features and ore-forming rule of the Shabaoshi gold depositin Mohe，Heilongjian[J]. Gold Science and Technology，2007，15(2)：20−25.

[20] 石永文，李向文，于雷，等. 砂宝斯金矿地质构造特征及工程地质岩组划分[J]. 现代矿业，2016，32(6)：145−148.

[21] 李向文，张恒志，石永文，等. 黑龙江砂宝斯金矿环境工程地质问题研究[J]. 黄金科学技术，2011，19(6)：75−78.

LI Xiangwen， ZHANG Hengzhi， SHI Yongwen， et al. Study onthe problems of environmental engineering geology in Shabaosi gold deposit，Heilongjiang Province[J]. Gold Science and Technology，2011，19(6)：75−78.

[22] 窦波元，闫永生，李向文，等. 黑龙江砂宝斯金矿床水资源质量评价[J]. 黄金科学技术，2012，20(3)：90−94.

DOU Boyuan，YAN Yongsheng，LI Xiangwen，et al. Evaluation on quality of water resources of Shabaosi gold deposit，Heilongjiang Province[J]. Gold Science and Technology，2012，20(3)：90−94.

[23] 张恒志，李向文，王希才，等. 黑龙江省砂宝斯金矿含水层特征及矿床充水条件研究[J]. 黄金科学技术，2008，16(3)：21−25.

ZHANG Hengzhi，LI Xiangwen，WANG Xicai，et al. Aquifer characteristics and water-filling conditions of ore-bed about shabaosi gold mine，Heilongjiang Province[J]. Gold Science and Technology，2008，16(3)：21−25.

[24] 石永文. 大兴安岭北部砂宝斯金矿水文地质特征及供水方向评价[J]. 黄金科学技术，2009，17(5)：46−48.

SHI Yongwen. Evaluation on hydrogeology features and watersupply direction of Shabaosi gold mine in north Daxing’anling[J]. Gold Science and Technology，2009，17(5)：46−48.

[25] 刘辉，李国强，朱晓峻，等. 基于深度学习的井筒变形预测模型与应用[J/OL]. 煤炭学报，2024：1–17[2024-06-24]. http://doi.org/10.13225/j.cnki.jccs.2024.0069.

LIU Hui，LI Guoqiang，ZHU Xiaojun，et al. Exploration and application of deep learning based wellbore deforemation forecasting mode[J/OL]. Journal of China Coal Sociaty，2024：1–17[2024-06-24]. http://doi.org/10.13225/j.cnki.jccs.2024.0069.

[26] 朱景宝，刘赫奕，栾世成，等. 基于机器学习和迁移学习的现地地震动峰值预测[J/OL]. 地球科学，2024：1–23[2024-07-22]. https://link.cnki.net/urlid/42.1874.P.20240719.1309.002.

ZHU Jingbao，LIU Heyi，LUAN Shicheng，et al. Prediction of on-site peak ground motion based on machine learning and transfer learning[J/OL]. Earth Science，2024：1–23[2024-07-22]. https://link.cnki.net/urlid/42.1874.P.20240719.1309.002.

[27] SCHUSTER M，PALIWAL K K. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing，1997，45(11)：2673−2681.

[28] 钟艳. 基于CNN-LSTM的复合神经网络在油田污水系统故障诊断中的应用[J]. 吉林大学学报(信息科学版)，2024，42(5)：817−828.

ZHONG Yan. Application of composite neural network based on CNN-LSTM in fault diagnosis of oilfield wastewater system[J]. Journal of Jilin University (Information Science Edition)，2024，42(5)：817−828.

[29] 王世文. 陕北某煤矿矿坑涌水量预测[D]. 石家庄：石家庄经济学院，2013.

WANG Shiwen. Prediction of water discharges of a coalmine in northern Shaanxi[D]. Shijiazhuang：Hebei GEO University，2013.

[30] 石炳兴，沈毅，石光，等. 宝日希勒露天煤矿涌水量预测研究[J]. 煤炭工程，2024，56(8)：93−98.

SHI Bingxing，SHEN Yi，SHI Guang，et al. Water inflow prediction Baorixile open-pit mine[J]. Coal Engineering，2024，56(8)：93−98.

[31] 周全超，王丹丹，党志伟，等. 矿井涌水量预测方法及对比分析[J]. 煤炭科学技术，2024，52(增刊1)：211−220.

ZHOU Quanchao，WANG Dandan，DANG Zhiwei，et al. Research and comparative analysis of mine water inflow prediction method：A case study of Donggou coal mine in Xinjiang[J]. Coal Science and Technology，2024，52(Sup.1)：211−220.

[32] 张子祥，李文鑫. 矿井涌水量预测方法的比较研究：以甘肃大庄煤矿为例[J]. 西北地质，2022，55(3)：355−360.

ZHANG Zixiang，LI Wenxin. Comparative study on prediction methods of mine inflow：A case study of Dazhuang coal mine in Gansu Province[J]. Northwestern Geology，2022，55(3)：355−360.

[33] 闫永生，李向文，李师白，等. 吉林省复兴村金矿区水文地质特征及涌水量预测[J]. 黄金，2015，36(11)：23−27.

YAN Yongsheng，LI Xiangwen，LI Shibai，et al. Hydrogeological characteristics of Fuxingcun gold deposit in Jilin Province and its prediction of water yield[J]. Gold，2015，36(11)：23−27.

[34] 吴煌. 黔北龙潭组煤层开采覆岩变形破坏特征及涌水量预测：以绿塘煤矿为例[D]. 贵阳：贵州大学，2023.