# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
###### Abstract  
A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the complexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accurately delineate the geological features from 3D seismic data. To generate labeling data for training the supervised Convolutional Neural Network (CNN) model, we propose an efficient workflow based on unsupervised learning. This workflow utilized seismic attributes and KernelPCA to enhance the visualization of geological targets and clustering the features into binary classes using K-means approach. With this workflow, we are able to develop a data-driven model and reduce human subjectivity. We applied this technique in two cases with different geological settings. The synthetic data and the real seismic investigation from the A Field in the Malay Basin. From this application, we demonstrate that our CNN-based model is highly accurate and consistent with the previous manual interpretation in both cases. In addition to qualitatively evaluating the interpretations, we further extract the predicted result into a 3D geobody. This result could help the interpreter focus on tasks requiring human expertise and aid the model's prediction in the next studies. seismic; geological features detection; artificial intelligence; unsupervised learning; convolutional neural network +
Footnote †: journal: _Applied sciences_  
A particular supervised learning method has shown tremendous success in detect geological features on seismic data, including the detection of salt bodies [4; 5; 6], the delineation of faults [7; 8], and classification different type of seismic facies [9; 10; 11; 12]. Several studies also used the unsupervised learning method to detect the geological features, for example, by using principal component analysis [13], K-means clustering [14; 15], a self-organizing map (SOM) [16], and a convolutional autoencoder [17]. In this paper, we consider combining supervised and unsupervised learning techniques into semi-supervised learning methods. By using an unsupervised learning approach, we will design an enhanced workflow to generate the label data that will be used to develop the supervised model. This workflow starts by applying several seismic attributes to the seismic data to help visually enhance or quantify geological features of interest. Then, we used the dimensionality reduction technique to reduce and combine multi-attribute analysis into some various principal components. Finally, using these principal components, we applied a clustering technique to generate the labeling data. This proposed workflow can help us generate a fair amount of data for the model building process, where manual labeling is challenging. Additionally, the unsupervised learning approach is a more data-driven way to determine the geological labels present in the seismic data, without being affected by interpreters' biases. For the model building, we applied a supervised learning approach by using one of the deep learning techniques called Convolutional Neural Network (CNN). Consider the geological features detection in seismic data as an image segmentation problem. We solve this problem using a modified CNN's architecture called U-Net [18]. In order to get the accurate prediction, we fine-tuned the model hyperparameters, including network architecture, loss technique, activation function, and many more [19]. We also applied the transfer learning technique by reusing the comprehensively pre-trained model in other datasets to our architecture's backbone [20]. This paper proposes an enhanced workflow based on a semi-supervised learning approach to detect geological features in 3D seismic data automatically. The structure of this paper is organized as follows. First, we start by discussing two 3D seismic data, including synthetic and real seismic surveys, with the related geological background.