# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 1 Introduction
In particular, the current version has been expanded to include: First, additional techniques to improve the classification performance of CNNs; second, a quadratic pooling strategy that was reviewed and tested in order to further increase the discriminative capabilities of the CNN-features [15]; and third, new experiments were conducted and analyzed to consider how improving CNNs in classification systems that classify CNN-features using conventional classifiers changes the system performance. In this paper, we focus on the following research issues regarding the use of CNN-feature vectors. First, we attempt to hybridize deep-learning techniques and pattern recognition (PR) methods for seismic patch classification. Traditional PR proceeds in two stages: data representation and feature classification, while CNN is implemented in two stages: feature extraction and classification. It is well known that the representation phase of PR is sensitive to the domain properties, while the classification phase of a CNN has a long training time. The main issue of this paper is the hybridization of the above two stages to address these shortcomings. On the basis of this hybrid strategy, we can combine the feature extraction capabilities of CNNs with the various classifiers that have been developed so far and can utilize the appropriate classifiers for applications. Second, we attempt to adopt transfer learning [16] in model learning for seismic patch classification. In general, while artificially generating experimental data is not expensive, there is a limit to presenting the detailed state of the real world. On the other hand, the real-world data can reflect the true state well, but it is difficult to obtain a sufficient amount. The second research point of the paper is the use of the transfer learning technique in the learning of models for the extraction of CNN-features. That is, we first learned CNN models using well-prepared artificial data, and then leveraged them as pre-trained models to learn classification models on real-world data. The third issue of the paper is the application of data complexity in an effort to find reasons why this hybrid approach leads to improved classification results. To achieve our purpose, we measured the data complexity of the feature vectors extracted with traditional methods, including PCA, and compared them with that of the CNN-features. Finally, the fourth concern of the paper is to represent the seismic patches in CNN-features. To avoid the curse of dimensionality in image classification, various approaches have been used so far. The CNN-features can be utilized regardless of their sensitivity to the cardinality and dimensions of the dataset since they are extracted from CNNs. The remainder of the paper is organized as follows: in Section 2, a brief introduction to the latest findings on seismic fault detection, using CNN-features and data complexities, is provided; in Section 3, a classification method related to this empirical study and the structure of seismic patch image data are presented, in turn; in Section 4, experimental studies, such as experimental data and methods, and classification accuracy rates and data complexity measurements, are described in detail; in Section 5, conclusive arguments and limitations that are worthy of further research are summarized.