(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 2, 2022 56 | Page www.ijacsa.thesai.org Combining Multiple Seismic Attributes using Convolutional Neural Networks Abrar Alotaibi 1 , Mai Fadel 2 , Amani Jamal 3 , Ghadah Aldabbagh 4 Computer Science Department, Faculty of Computing and Information Technology King Abdulaziz University, Jeddah, Saudi Arabia 1, 2, 3, 4 Computer Science Department, College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia 1 Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA 4 Abstract—Seismic exploration involves estimating the properties of the Earth's subsurface from reflected seismic waves then visualizing the resulting seismic data and its attributes. These data and derived seismic attributes provide complementary information and reduce the amount of time and effort for the geoscientist. Multiple conventional methods to combine various seismic attributes exist, but the number of attributes is always limited, and the quality of the resulting image varies. This paper proposes a method that can be used to overcome these limitations. In this paper, we propose using Deep Learning-based image fusion models to combine seismic attributes. By using convolutional neural network (CNN) capabilities in feature extraction, the resulting image quality is better than that obtained with conventional methods. This work implemented two models and conducted a number of experiments using them. Several techniques have been used to evaluate the results, such as visual inspection, and using image fusion metrics. The experiments show that the Image-fusion Framework, using the Image Fusion Framework Based on CNN (IFCNN) approach, outperformed all other models in both quantitative and visual analysis. Its QAB/F and MS-SSIM scores are 50% and 10%, respectively, higher than all other models. Also, IFCNN was evaluated against the current state-of-the-art solution, Octree, in a comparative study. IFCNN overcomes the limitation of the Octree method and succeeds in combining nine seismic attributes with a better-combining quality, with QAB/F and NAB/F scores being 40% higher. Keywords—CNNs; neural networks; seismic attributes; seismic images; image fusion I. INTRODUCTION Seismic data is a major source of information for Earth subsurface exploration and visualization. To gather seismic data, seismic waves are sent into the Earth’s subsurface, and the resulting reflection is recorded. Using these reflections, the underlaying structural information is obtained and the earth subsurface can be modeled and visualized [1]. The data that are obtained from the seismic data, to supplement and enhance the geological/geophysical information, are referred to as seismic attributes [2]. They help to make the process of visualization more informative. The current process used by engineers, archeologists, geologists, and other scientific scholars to develop accurate representations of the Earth's subsurface involves looking at the seismic images and their related seismic attributes, which is followed by the interpretation of huge volumes of data. The process is, however, bulky and makes it difficult to combine the various views into one comprehensive view that can efficiently exploit all the data included in each individual view and reduce the time taken in the process. Various scholars have made major contributions to address the challenge of combining seismic attributes, including Octree, principal component analysis (PCA), cross-plotting, and volume blending [3], [4]. The most recent work by Al- Dossari et al. [4], show how the Octree color quantization algorithm can be extended to enhance the combined seismic attributes. However, the method has some limitations. For instance, the number of attributes is limited to a maximum of eight, the structural disposition of the attributes can affect the results, and the result of the combined image includes artifacts. Image fusion can be described as the process of combining more than one input image that contains complementary information from related scenes, thus producing a composite image [5]. The input images are obtained from matching imaging devices, including various types of imaging devices, or from various other parameters such as infrared cameras and satellites. The resultant composite image is more useful in terms of the included information as compared to the individual images [6]. The techniques used in image fusion offer many benefits in different image processing tasks that rely on viewing more than one image of the same scene, such as object recognition and detection, as well as areas like digital photography and remote sensing, among others. Merging the key information of various input images into one fused image can be helpful in reducing the challenge of wasted time and enhancing the final results of the work [5]. The data enrichment offered by seismic attributes of seismic images is the same as in various other image fusion tasks, like remote sensing and medical imaging. The recent development of deep learning (DL) has led to various experts in the field developing different image fusion techniques using the new technology. In this field, Machine Learning algorithms, afforded by deep learning, along with neural networks, are used to extract data and image representations. The use of Convolutional Neural Networks (CNN) is important in solving the conventional, manual method challenge of designing fusion techniques and choosing