Empirical Evaluation on Utilizing CNN-features for Seismic Patch Classification

This paper empirically evaluates two kinds of features, which are extracted respectively with neural networks and traditional statistical methods, to improve the performance of seismic patch image classification. The convolutional neural networks (CNNs) are now the state-of-the-art approach for a lot of applications in various fields, including computer vision and pattern recognition. In relation to feature extraction, it turns out that generic feature descriptors extracted from CNNs, named CNN-features, are very powerful. It is also well known that combining CNN-features with traditional (non)linear classifiers improves classification performance. In this paper, the above classification scheme was applied to seismic patch classification application. CNN-features were acquired first and then used to learn SVMs. Experiments using synthetic and real-world seismic patch data demonstrated some improvement in classification performance, as expected. To find out why the classification performance improved when using CNN-features, data complexities of the traditional feature extraction techniques like PCA and the CNN-features were measured and compared. From this comparison, we confirmed that the discriminative power of the CNN-features is the strongest. In particular, the use of transfer learning techniques to obtain CNN's architectures to extract the CNN-features greatly reduced the extraction time without sacrificing the discriminative power of the extracted features.