# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 1 Introduction  
Seismic faults are important subsurface structures that have significant geologic implications for hydrocarbon accumulation and migration in a petroleum reservoir. On the basis of these characteristics, it is very important to detect faults with advanced techniques. Recently, seismic fault detection using deep-learning techniques has been actively studied [1, 2, 3, 4, 5]. In this approach, seismic images are first divided into patches of a certain size. The fault detection problem then becomes a two-class classification problem that classifies fault and nonfault (normal) patches. The fault detection problem can be solved by identifying the location of the patches classified as abnormal patches in the fault line. This paper focuses on the classification of patch data. First, feature vectors were extracted from seismic patch data and were then classified as fault and nonfault patches using existing classifiers to find fault lines. Convolutional neural networks (CNNs) are now state-of-the-art approaches for a lot of applications. On the basis of their good performance, CNNs have recently been used to detect seismic faults [4]. However, two constraints can be found in this approach: one is the need to provide a huge amount of interpreted data (e.g., fault and nonfault patches); the other is the significant amount of time required to process them. To address the first, a synthetic dataset, having simple fault geometries, was built. Therein, the input to the CNN was the seismic amplitude only; that is, the approach did not require the calculation of the other seismic attributes, but the second constraint remains without any solution. As is commonly known, CNNs take a tremendous amount of time to learn when allowing \"sufficient\" training data. Recently, it has been observed that the convolutional(C) and fully connected (FC) layers take the most time to run [6]. In particular, since the latter is responsible for the multiplication of large-scale matrices, it consumes up to almost 60% of the computation time. From the above review results, as well as from the findings in [6; 7; 8; 9; 10; 11], we may consider replacing the FC layers responsible for classifying seismic patch data in a CNN with an existing classifier, such as support vector machines (SVMs). The role of the C layer in this framework corresponds to, for example, the role of the principal component analysis (PCA) in traditional statistical-based classification. The use of SVMs instead of FC layers is known to improve the classification accuracy [7; 12], but no analysis has been made on why. Rather than embarking on a general analysis, in this paper, we will consider a comparative study that will be taken as the basis for the above improvements. In addition, the measures of the data complexity can be used to estimate the difficulty in separating the sample points into their expected classes. Especially, it has been reported that the measurements can be performed to figure out a variety of characteristics related to data classification [13]. From this point of view, to derive an intuitive comparison and to answer the above question, we will consider the complexity measurements. In this paper, we use CNN-features in seismic patch classification. Using this feature vector, we can avoid the problems of a long learning time and a lack of training data, without deteriorating the classification performance. We also analyzed the data complexity to compare the discriminating powers of the features. A preliminary short version of this paper was published as a conference paper [14].