Classification of seabed sediment using the fuzzy C mean clustering and support vector machine methods
You Jia-chu · Haiyang kexue · 2014
Basis on the previous research about the classification of sediment, Here, for the first time, we proposed the application of numerical simulation technology in classification of seabed sediment. Compared to the currently used flume method in the classification of sediment, we proposed the usage of computer technology to simulate the seismic acquisition process in practical exploration. In the classification and recognition algorithms, the support vector machine and fuzzy C mean clustering methods(FCM) were used to classify the data acquired. To maximize the accuracy of support vector machine(SVM), we introduced the evolutionary difference algorithm to optimize the values of the key parameters of support vector machine classification model. Then, to further study the stability of the methods, 10%, 30% and 50% of Gaussian white noise was added into the original data. After full analysis of the advantages and disadvantages of both approaches from its principle and classification accuracy, we designed a two-step classifier which combined the fuzzy C mean clustering and support vector machine. Firstly, the unsupervised classification algorithm, FCM, was used to classify the data preliminarily, and screened the samples with good clustering. Secondly, those selected data in step 1 were served as train samples of support vector machine model. The differential evolution algorithm was used to optimize the key parameters of support vector machine model, then the optimized support vector machine model was used to classify the remaining data. Finally, Based on the repeatable, convenient characters of the computer simulation and the relevant high accuracy and the robustness of FCM-SVM, a total solution of a classification, which will be easier, deeper, further to study the feature of reflection from sediment is proposed in the article.