Seismic image identification and detection based on Tchebichef moment invariant

Simple item page
dc.contributor.authorLu, Andong
dc.contributor.authorHonarvar Shakibaei Asli, Barmak
dc.date.accessioned2023-09-18T15:30:17Z
dc.date.available2023-09-18T15:30:17Z
dc.date.issued2023-08-31
dc.description.abstractThe research focuses on the analysis of seismic data, specifically targeting the detection, edge segmentation, and classification of seismic images. These processes are fundamental in image processing and are crucial in understanding the stratigraphic structure and identifying oil and natural gas resources. However, there is a lack of sufficient resources in the field of seismic image detection, and interpreting 2D seismic image slices based on 3D seismic data sets can be challenging. In this research, image segmentation involves image preprocessing and the use of a U-net network. Preprocessing techniques, such as Gaussian filter and anisotropic diffusion, are employed to reduce blur and noise in seismic images. The U-net network, based on the Canny descriptor is used for segmentation. For image classification, the ResNet-50 and Inception-v3 models are applied to classify different types of seismic images. In image detection, Tchebichef invariants are computed using the Tchebichef polynomials’ recurrence relation. These invariants are then used in an optimized multi-class SVM network for detecting and classifying various types of seismic images. The promising results of the SVM model based on Tchebichef invariants suggest its potential to replace Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs) for seismic image detection. This approach offers a more efficient and dependable solution for seismic image analysis in the future.en_UK
dc.identifier.citationLu A, Honarvar Shakibaei Asli B. (2023) Seismic image identification and detection based on Tchebichef moment invariant. Electronics, Volume 12, Issue 17, August 2023, Article number 3692en_UK
dc.identifier.issn2079-9292
dc.identifier.urihttps://doi.org/10.3390/electronics12173692
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20237
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectseismic imagesen_UK
dc.subjectmoment functionsen_UK
dc.subjectmoment invariantsen_UK
dc.subjectTchebichef momentsen_UK
dc.subjectimage enhancementen_UK
dc.titleSeismic image identification and detection based on Tchebichef moment invarianten_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Seismic_image_identification-2023.pdf
Size:
121.08 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)