Lithofacies Recognition Based on Extreme Learning Machine

Abstract:

To overcome the problem of lower training speed and difficulty parameter selection in traditional support vector machine (SVM), a method based on extreme learning machine (ELM) for lithofacies recognition is presented in this paper. ELM is a new learning algorithm with single-hidden layer feedforward neural networks (SLFNN). Not only it can simplify the parameter selection process, but also improve the training speed of the network learning. By determining the optimal parameters, the lithofacies classification model is established, and the classification result of ELM is also compared to traditional SVM. The experimental results show that, ELM with less number of neurons has similar classification accuracy compared to SVM, and it is easier to select the parameters which significantly reduce the training speed. The feasibility of ELM for lithofacies recognition and the availability of the algorithm are verified and validated

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 1762-1767
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.1762
• Online since: December 2012
• Authors: Ya Juan Tian, Hua Xian Pan, Xuan Chao Liu, Guo Jian Cheng
• Keywords: Extreme Learning Machine, Feedforward Neural Networks, Lithofacies Recognition, Support Vector Machine (SVM)

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