A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM
| Autor: | Kewen Xia, Ziping He, Li Wang, Yongke Pan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Physics and Astronomy (miscellaneous) Computer science General Mathematics Catfish effect Cloud computing 02 engineering and technology cloud theory 020901 industrial engineering & automation Convergence (routing) 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) QA1-939 oil layer recognition Layer (object-oriented design) whale optimization algorithm semi-supervised support vector machine catfish effect business.industry Swarm behaviour Pattern recognition Support vector machine ComputingMethodologies_PATTERNRECOGNITION Chemistry (miscellaneous) Kernel (statistics) Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business Mathematics |
| Zdroj: | Symmetry, Vol 13, Iss 757, p 757 (2021) Symmetry; Volume 13; Issue 5; Pages: 757 |
| ISSN: | 2073-8994 |
| Popis: | The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition. |
| Databáze: | OpenAIRE |
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