Enhancement of 3D Seismic Images using Image Fusion Techniques

Alotaibi, Abrar; Fadel, Mai; Jamal, Amani; Aldabbagh, Ghadah

doi:10.14569/ijacsa.2021.0120143

Cited by 1 publication

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model was also tested against state-of-the-art models used in multi-focus image fusion, multi-exposure image fusion, infrared-visible image fusion, and medical image fusion, and outperformed all of them. Alotaibi et al [9] proposed the use of DL models to combine seismic attributes. The work primarily investigated the performance of current state-of-the-art models, pre-trained for specific types of image fusion in combining three seismic attributes.…”

Section: B General Image Fusionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Alotaibi¹,

Fadel²,

Jamal³

et al. 2022

IJACSA

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: B General Image Fusionmentioning

confidence: 99%

“…This paper includes experiments that compared the proposed technique by implementing two fusion models with other fusion models used previously by Alotaibi et al [9], and then compares their results. It also compares the model's results against the results of Octree.…”

Section: Introductionmentioning

confidence: 99%