Due to the geological complexities of ore body formation and limited borehole sampling, this paper propos- es a robust weighted least square support vector machine （LS-SVM） regression model to solve the ore grade es...Due to the geological complexities of ore body formation and limited borehole sampling, this paper propos- es a robust weighted least square support vector machine （LS-SVM） regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each incomplete sample. Then, a weighted K-nearest neighbor （WKNN） algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimiza- tion （PSO） algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by random division. Coupled simulated annealing （CSA） and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameter- s in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight （IDW）, ordinary kriging （OK）, and back propagation （BP） neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the other methods, and has strong predictive and generalization ability.展开更多
基金Project of China Ocean Association under contact No. DYXM-125-25-02Independent Research Project of Tsinghua University under contact Nos 2010THZ07002 and 2011THZ07132
文摘Due to the geological complexities of ore body formation and limited borehole sampling, this paper propos- es a robust weighted least square support vector machine （LS-SVM） regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each incomplete sample. Then, a weighted K-nearest neighbor （WKNN） algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimiza- tion （PSO） algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by random division. Coupled simulated annealing （CSA） and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameter- s in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight （IDW）, ordinary kriging （OK）, and back propagation （BP） neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the other methods, and has strong predictive and generalization ability.
