基于粒子群优化算法和ANFIS的矿体品位插值

Grade interpolation of orebody based on particle swarm optimization algorithm and ANFIS

地质模型在矿产勘探与开发中具有重要作用,但在矿山生产实践中,由于成本和技术等诸多因素影响,很难获得整个区块的地质数据,而且传统插值方法依靠经验确定参数有很大局限性。提出将粒子群优化算法(PSO)和自适应神经模糊推理系统(ANFIS)应用到矿体品位插值中,利用粒子群优化算法的快速搜索能力,神经网络的学习机制和模糊系统的语言推理能力等优势构建PSO-ANFIS品位插值模型,并借助MATLAB生成571组样本数据作为输入空间对模型进行训练,其中每一个训练样本由待估点三维坐标及真实值和其周围8个样品点组成,最后用训练后的PSO-ANFIS模型对待估点进行品位插值,并与距离幂次反比插值法进行对比,其均方根误差(RMSE)提高了近15%,验证了该模型的可行性和有效性。

Geological model plays an important role in mineral exploration and development,but in the practice of mine production,because of the influence of cost and technology,it is difficult to obtain the geological data of the whole block,and the spatial interpolation is an important means to solve this problem.The particle swarm optimization(PSO)and adaptive neuro-fuzzy inference system(ANFIS)were applied to the grade interpolation of orebody,which overcomes the limitation of traditional interpolation method based on empirical determination of parameters,PSO-ANFIS grade interpolation model was constructed by using the fast searching ability of particle swarm optimization,the learning mechanism of neural network and the language reasoning ability of fuzzy system.Selecting 571 groups of sample points as training data to train the model with the cross verification method in MATALB,each of these training samples consists of three-dimensional coordinates and true values of the estimated points and eight surrounding sample points,finally,the PSO-ANFIS model was used to evaluate the evaluation point and the mean square root error(RMSE)was improved by comparing with the distance power-time inverse interpolation method,which is nearly 15%.The feasibility and effectiveness of the model were validated.