Inversion of permeability coefficient based on adaptive differential hybrid butterfly particle algorithm
-
摘要:
准确获取渗透系数等含水层水文参数是矿井水害防治的前提，但传统配线法、图解法等反演方法在计算速度、结果精度等方面表现略差。为提升含水层参数反演计算的可靠性，此次研究针对水文地质参数本身特性，设计了一种新的渗透系数反演模型，即自适应差分混合蝴蝶粒子算法（adaptive differential hybrid butterfly particle algorithm，ADHBPA)。模型采用拉丁超立方采样策略、双曲余弦自适应函数、差分变异策略以及逐维变异策略进行算法优化，克服了水文地质参数反演过程中的空间异质性和时间动态性等问题，提高全局搜索与局部搜索间的平衡能力。以板集矿区24 口钻孔抽水试验数据为例开展验证，结果显示，ADHBPA模型计算降深与观测降深拟合最大误差为0.93 m，平均误差率仅0.15%，其余算法平均误差率均在30%～50%，表明多策略协同优化显著增强了算法跳出局部最优的能力，实现了有限数据前提下对含水层渗透系数的快速高精度反演。该算法为矿井水害风险评价与防治水方案制定提供了高效可靠的技术支撑。
Abstract:Accurate determination of aquifer hydrological parameters, such as permeability coefficient, is essential for effective mine water hazard prevention and control. However, traditional inversion methods such as the fitting curve method and graphical method exhibit shortcomings in computational speed and accuracy. To enhance the reliability of aquifer parameter inversion calculations, this study proposed a novel permeability coefficient inversion model, the adaptive differential hybrid butterfly particle algorithm (ADHBPA), specifically tailored to the characteristics of hydrogeological parameters. The model incorporates Latin hypercube sampling, a hyperbolic cosine adaptive function, differential mutation strategy, and dimension-wise variation strategy. The model effectively addressed the spatial heterogeneity and temporal dynamics inherent in hydrogeological parameter inversion, thereby improving the balance between global exploration and local exploitation. Using the pumping test data from 24 boreholes in the Banji mining area, the ADHBPA model achieved a maximum inversion error of 0.93 m and an average error rate of just 0.15%. In contrast, conventional algorithms produced average error rates ranging from 30% to 50%. These results highlight the algorithm's strong capability in avoiding local optima and performing high-precision parameter inversion, even under data-scarce conditions. The proposed algorithm provides efficient and reliable technical support for mine water hazard risk assessment and water control planning.
-
-
表 1 抽水试验数据
Table 1. Pumping test data
钻孔编号 含水层厚度/m 钻孔半径/m 降深/m 涌水量/（L·s−1） 1 18.650 0.057 59.750 0.128 2 97.300 0.046 159.700 0.002 3 34.250 0.057 86.590 0.013 4 36.150 0.057 160.280 0.025 5 30.700 0.057 56.180 0.083 6 55.650 0.057 54.250 0.049 7 34.400 0.058 56.100 0.399 8 27.250 0.057 33.310 0.038 9 33.730 0.057 50.570 0.179 10 22.000 0.066 39.380 0.024 11 32.500 0.058 43.460 0.106 12 52.640 0.054 42.260 0.019 13 93.750 0.054 44.100 1.208 14 63.400 0.054 68.710 0.005 15 15.900 0.057 52.720 0.483 16 13.600 0.057 48.000 0.150 17 102.600 0.054 93.080 0.771 18 24.750 0.057 51.480 0.223 19 24.000 0.057 66.190 0.033 20 128.570 0.057 52.020 0.036 21 7.500 0.057 67.860 0.111 22 83.250 0.057 57.150 0.567 23 14.500 0.057 56.080 0.031 24 18.650 0.057 59.790 0.120 
下载: 导出CSV
表 2 测试函数fun1～fun4
Table 2. Test function
函数名称 函数表达式 变量范围 $ fun1 $ $ f_1=\displaystyle\sum_{i=1}^nx_i^2 $ $ \left[-100,100\right] $ $ fun2 $ $ {f}_{2}=\displaystyle\sum _{i=1}^{n}\left|{x}_{i}\right|+\displaystyle\prod _{i=1}^{n}\left|{x}_{i}\right| $ $ \left[-100,100\right] $ $ fun3 $ $ {f}_{3}=\displaystyle\sum _{i=1}^{n}{\left[\displaystyle\sum _{j-1}^{i}{x}_{j}\right]}^{2} $ $ \left[-100,100\right] $ $ fun4 $ $ {f}_{4}={max}_{i}\left\{\left|{x}_{i}\right|，1\leqslant i\leqslant n\right\} $ $ \left[-100,100\right] $
下载: 导出CSV
表 3 板集区域计算降深与观测降深试验结果
Table 3. Hydrological inversion results of BanJi
算法 中位数绝对误差/m 标准差/m 最大误差/m 平均误差率/% BOA 16.73 30.77 149.15 40.94 AGSABOA 10.30 23.19 105.46 37.91 CABOA 23.30 17.80 67.83 48.22 HPSBA 5.21 24.07 85.60 29.40 ADHBPA 0.03 0.21 0.93 0.15
下载: 导出CSV
表 4 某层位渗透系数值
Table 4. Permeability coefficient value of a layer
孔号 涌水量/（L·s−1） 含水层层位 渗透系数/（m·d−1） 1 0.128 9煤顶板砂岩 0.012798 3 0.013 9煤顶板砂岩 0.000410 4 0.025 9煤顶板砂岩 0.000421 15 0.128 9煤顶板砂岩 0.013468 16 0.483 9煤顶板砂岩 0.019361 24 0.031 9煤顶板砂岩 0.003769
下载: 导出CSV
-
[1] GUO Lidan,XIA Ziqiang,YU Lanlan,et al. Ecological significance of instream hydrological statistical parameters[J]. Journal of Hydrologic Engineering,2013,18(9):1088 − 1097. doi: 10.1061/(ASCE)HE.1943-5584.0000752
[2] MOHAMMED M A A,FLORES Y G,SZABÓ N P,et al. Assessing heterogeneous groundwater systems:Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters[J]. Scientific Reports,2024,14(1):7314. doi: 10.1038/s41598-024-57435-x
[3] LEWIS A Z. A method using drawdown derivatives to estimate aquifer properties near active groundwater production well fields[D]. Fort Collins:Colorado State University,2014.
[4] WU C M,YEH T C J,ZHU Junfeng,et al. Traditional analysis of aquifer tests:Comparing apples to oranges?[J]. Water Resources Research,2005,41(9):W09402.
[5] ZECH A,MÜLLER S,MAI J,et al. Extending theis' solution:Using transient pumping tests to estimate parameters of aquifer heterogeneity[J]. Water Resources Research,2016,52(8):6156 − 6170. doi: 10.1002/2015WR018509
[6] VRUGT J A,STAUFFER P H,WÖHLING T,et al. Inverse modeling of subsurface flow and transport properties:A review with new developments[J]. Vadose Zone Journal,2008,7(2):843 − 864. doi: 10.2136/vzj2007.0078
[7] LIU Shuqun,SUN Kun. Hand-painted curve fitting method based on NURBS curve[J]. Advanced Materials Research,2014,1049/1050:1385 − 1388. doi: 10.4028/www.scientific.net/AMR.1049-1050.1385
[8] RAJESH M,KASHYAP D,HARI PRASAD K S. Estimation of unconfined aquifer parameters by genetic algorithms[J]. Hydrological Sciences Journal,2010,55(3):403 − 413. doi: 10.1080/02626661003738167
[9] BATENI S M,MORTAZAVI-NAEINI M,ATAIE-ASHTIANI B,et al. Evaluation of methods for estimating aquifer hydraulic parameters[J]. Applied Soft Computing,2015,28:541 − 549. doi: 10.1016/j.asoc.2014.12.022
[10] CUTHBERT M O. An improved time series approach for estimating groundwater recharge from groundwater level fluctuations[J]. Water Resources Research,2010,46(9):W09515.
[11] SOUPIOS P M,KOULI M,VALLIANATOS F,et al. Estimation of aquifer hydraulic parameters from surficial geophysical methods:A case study of Keritis Basin in Chania (Crete–Greece)[J]. Journal of Hydrology,2007,338(1/2):122 − 131.
[12] GHORBANIDEHNO H,KOKKINAKI A,LEE J,et al. Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology[J]. Journal of Hydrology,2020,591:125266. doi: 10.1016/j.jhydrol.2020.125266
[13] KARAHAN H,AYVAZ M T. Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks[J]. Hydrogeology Journal,2008,16(5):817 − 827. doi: 10.1007/s10040-008-0279-0
[14] MANISHA P J,RASTOGI A K,MOHAN B K. Critical review of applications of Artificial Neural Networks in groundwater hydrology[C]//Proceedings of the 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG). Mumbai:International Association for Computer Methods and Advances in Geomechanics,2008:2463 − 2474.
[15] KARABOGA D,BASTURK B. A powerful and efficient algorithm for numerical function optimization:Artificial bee colony (ABC) algorithm[J]. Journal of Global Optimization,2007,39(3):459 − 471. doi: 10.1007/s10898-007-9149-x
[16] 张铃,张钹. 遗传算法机理的研究[J]. 软件学报,2000,11(7):945 − 952. [ZHANG Ling,ZHANG Bo. Research on the mechanism of genetic algorithms[J]. Journal of Software,2000,11(7):945 − 952. (in Chinese with English abstract)]
ZHANG Ling, ZHANG Bo. Research on the mechanism of genetic algorithms[J]. Journal of Software, 2000, 11（7）: 945 − 952. （in Chinese with English abstract）
[17] COELHO L D S,MARIANI V C. Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints[J]. Energy Conversion and Management,2007,48(5):1631 − 1639. doi: 10.1016/j.enconman.2006.11.007
[18] SUN Zhe,WANG Yiwen,XIE Xiangpeng,et al. An event-triggered and dimension learning scheme WOA for PEMFC modeling and parameter identification[J]. Energy,2024,305:132352. doi: 10.1016/j.energy.2024.132352
[19] MCDERMOTT J. When and why metaheuristics researchers can Ignore “No Free Lunch” theorems[J]. SN Computer Science,2020,1(1):60. doi: 10.1007/s42979-020-0063-3
[20] GUO Rong,WANG Ran,YIN Juanjuan,et al. Fabrication and highly efficient dye removal characterization of Beta-Cyclodextrin-Based composite polymer fibers by electrospinning[J]. Nanomaterials,2019,9(1):127. doi: 10.3390/nano9010127
[21] 高文欣,刘升,肖子雅,等. 收敛因子和黄金正弦指引机制的蝴蝶优化算法[J]. 计算机工程与设计,2020,41(12):3384 − 3389. [GAO Wenxin,LIU Sheng,XIAO Ziya,et al. Butterfly optimization algorithm based on convergence factor and Gold sinusoidal guidance mechanism[J]. Computer Engineering and Design,2020,41(12):3384 − 3389. (in Chinese with English abstract)]
GAO Wenxin, LIU Sheng, XIAO Ziya, et al. Butterfly optimization algorithm based on convergence factor and Gold sinusoidal guidance mechanism[J]. Computer Engineering and Design, 2020, 41（12）: 3384 − 3389. （in Chinese with English abstract）
[22] 彭茂松. 蝴蝶优化算法改进及应用研究[D]. 南宁:广西民族大学,2023. [PENG Maosong. Research on improvement and application of butterfly optimization algorithm[D]. Nanning:Guangxi Minzu University,2023. (in Chinese with English abstract)]
PENG Maosong. Research on improvement and application of butterfly optimization algorithm[D]. Nanning: Guangxi Minzu University, 2023. （in Chinese with English abstract）
[23] 张孟健,汪敏,王霄,等. 混合粒子群-蝴蝶算法的WSN节点部署研究[J]. 计算机工程与科学,2022,44(6):1013 − 1022. [ZHANG Mengjian,WANG Min,WANG Xiao,et al. A hybrid particle swarm-butterfly algorithm for WSN node deployment[J]. Computer Engineering and Science,2022,44(6):1013 − 1022. (in Chinese with English abstract)] doi: 10.3969/j.issn.1007-130X.2022.06.008
ZHANG Mengjian, WANG Min, WANG Xiao, et al. A hybrid particle swarm-butterfly algorithm for WSN node deployment[J]. Computer Engineering and Science, 2022, 44（6）: 1013 − 1022. （in Chinese with English abstract） doi: 10.3969/j.issn.1007-130X.2022.06.008
[24] 阚昕. 地下水污染评估方法及其在城市化进程中的应用研究[J]. 黑龙江环境通报,2024,37(9):90 − 92. [KAN Xin. Research on groundwater pollution assessment methods and their application in urbanization process[J]. Heilongjiang Environmental Journal,2024,37(9):90 − 92. (in Chinese with English abstract)] doi: 10.3969/j.issn.1674-263X.2024.09.029
KAN Xin. Research on groundwater pollution assessment methods and their application in urbanization process[J]. Heilongjiang Environmental Journal, 2024, 37（9）: 90 − 92. （in Chinese with English abstract） doi: 10.3969/j.issn.1674-263X.2024.09.029
[25] SHI Y,EBERHART R. A modified particle swarm optimizer[C]//1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360). Piscataway:IEEE,1998:69 − 73.
[26] YANG Xueming,YUAN Jinsha,YUAN Jiangye,et al. A modified particle swarm optimizer with dynamic adaptation[J]. Applied Mathematics and Computation,2007,189(2):1205 − 1213. doi: 10.1016/j.amc.2006.12.045
[27] KOCIS L,WHITEN W J. Computational investigations of low-discrepancy sequences[J]. ACM Transactions on Mathematical Software,1997,23(2):266 − 294. doi: 10.1145/264029.264064
[28] 刘道华,陈良琼,胡秀云,等. 一种带停滞信息的自适应粒子群优化方法[J]. 西安电子科技大学学报,2016,43(3):120 − 124. [LIU Daohua,CHEN Liangqiong,HU Xiuyun,et al. Adaptive particle swarm optimization method with stagnancy information[J]. Journal of Xidian University,2016,43(3):120 − 124. (in Chinese with English abstract)]
LIU Daohua, CHEN Liangqiong, HU Xiuyun, et al. Adaptive particle swarm optimization method with stagnancy information[J]. Journal of Xidian University, 2016, 43（3）: 120 − 124. （in Chinese with English abstract）
[29] MITCHELL J K,HOOPER D R,CAMPENELLA R G. Permeability of compacted clay[J]. Journal of the Soil Mechanics and Foundations Division,1965,91(4):41 − 65. doi: 10.1061/JSFEAQ.0000775
[30] PANT M,ALI M,ABRAHAM A,et al. Mixed mutation strategy embedded differential evolution[C]//2009 IEEE Congress on Evolutionary Computation. Piscataway:IEEE,2009:1240 − 1246.
[31] PAN Zidong,LU Wenxi,WANG Han,et al. Fast inverse estimation of hydraulic conductivity field based on a deep convolutional-cycle generative adversarial neural network[J]. Journal of Hydrology,2022,613:128420. doi: 10.1016/j.jhydrol.2022.128420
[32] ZHENG Na,JIANG Simin,XIA Xuemin,et al. Efficient estimation of groundwater contaminant source and hydraulic conductivity by an ILUES framework combining GAN and CNN[J]. Journal of Hydrology,2023,621:129677. doi: 10.1016/j.jhydrol.2023.129677
[33] OSEI V,BAI Chunguang,ASANTE-DARKO D,et al. Evaluating the barriers and drivers of adopting circular economy for improving sustainability in the mining industry[J]. Resources Policy,2023,86:104168. doi: 10.1016/j.resourpol.2023.104168
-
图(9)
表(4)
计量
- 文章访问数: 36
- PDF下载数: 3
- 施引文献: 0