Fault-Seg-Net: A method for seismic fault segmentation based on multi-scale feature fusion with imbalanced classification

Li, Xiao; Li, Kewen; Xu, Zhi-Feng; Huang, Zongchao; Dou, Yiping

doi:10.1016/j.compgeo.2023.105412

Cited by 17 publications

(1 citation statement)

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“…In the oil and gas industry, An et al [13] is widely acknowledged as the software platform for seismic data interpretation, facilitating the opening and creation of horizontal slices from 3D seismic image datasets. Some 3D seismic image datasets, such as the Poseidon 3D seismic dataset from Australia, can be chosen from SEG Wiki [14]. These datasets serve as valuable resources for seismic interpretation purposes.…”

Section: Introductionmentioning

confidence: 99%

Seismic Image Identification and Detection Based on Tchebichef Moment Invariant

Lu,

Honarvar Shakibaei Asli

2023

Electronics

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The 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.

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Section: Introductionmentioning

confidence: 99%