文章基本信息

标题：Enhancement of 3D Seismic Images using Image Fusion Techniques
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作者：Abrar Alotaibi ; Mai Fadel ; Amani Jamal 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：1
页码：365-372
DOI：10.14569/IJACSA.2021.0120143
出版社：Science and Information Society (SAI)
摘要：Seismic images are data collected by sending seismic waves to the earth subsurface, recording the reflection and providing subsurface structural information. Seismic attributes are quantities derived from seismic data and provide complementary information. Enhancing seismic images by fusing them with seismic attributes will improve the subsurface visualization and reduce the processing time. In seismic data interpretation, fusion techniques have been used to enhance the resolution and reduce the noise of a single seismic attribute. In this paper, we investigate the enhancement of 3D seismic images using image fusion techniques and neural networks to combine seismic attributes. The paper evaluates the feasibility of using image fusion models pretrained on specific image fusion tasks. These models achieved the best results on their respective tasks and are tested for seismic image fusion. The experiments showed that image fusion techniques are capable of combining up to three seismic attributes without distortion, future studies can increase the number. This is the first study conducted using pretrained models on other types of images for seismic image fusion and the results are promising.
关键词：Image fusion; seismic image; seismic attribute; neural networks