【摘要】 矿产开采及地下工程要准确获得含水层水文地质参数才能做好涌水量预测及防治水害工作。基于此，将微粒群优化算法（PSO）用于水文地质参数识别和地下水位预测的工程实践进行研究，并重点介绍了PSO与Theis公式的耦合技术以及影响参数反演精度的因素，利用典型的抽水试验来进行实例分析证明了这种方法的有效性；结合工程实践阐述了该项技术的应用，对矿区疏干排水方案优化、地下水资源评价以及水位动态预测等方面所取得的良好效益，并对该技术在复杂补给条件下、强非均质含水层条件下存在的问题进行了探讨，最后提出了今后需要开展的工作方向，即针对算法参数开展智能调优，建立不同背景下的测试数据库，以及完善多种物理场耦合预测模型，提高该技术对复杂水文地质条件的适应性和预测可靠性。更多还原

【Abstract】 In the mining and underground engineering, it is necessary to accurately obtain the hydrogeological parameters of the aquifer in order to do a good job in water inflow prediction and water disaster prevention. Based on this, the particle swarm optimization algorithm( PSO) is used to study the engineering practice of hydrogeological parameter identification and groundwater level prediction, and the coupling technology of PSO and Theis formula and the factors affecting the accuracy of parameter inversion are mainly introduced. The effectiveness of this method is proved by typical pumping test.Combined with engineering practice, the application of this technology is expounded, and the good benefits obtained from the optimization of drainage scheme, evaluation of groundwater resources and dynamic prediction of water level in mining area are discussed. The problems existing in this technology under complex recharge conditions and strong heterogeneous aquifer conditions are discussed. Finally, the future work direction is put forward, that is, intelligent optimization of algorithm parameters, establishment of test database under different backgrounds, and improvement of various physical field coupling prediction models, so as to improve the adaptability and prediction reliability of this technology to complex hydrogeological conditions.更多还原