Seismic Imaging, Overview

Schuster, Gerard T.

doi:10.1007/978-3-030-10475-7_167-1

Cited by 2 publications

(2 citation statements)

References 86 publications

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“…Numerous studies have been conducted on seismic image processing to estimate the earth's rock parameters from seismic data. These properties primarily encompass the P-wave velocity, S-wave velocity, porosity, density, and anisotropic parameters [28]. The process of seismic image processing involves the comprehensive analysis, enhancement, interpretation, and extraction of valuable information from three-dimensional seismic datasets.…”

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%

Seismic Image Identification and Detection Based on Tchebichef Moment Invariant

Lu,

Honarvar Shakibaei Asli

2023

Electronics

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“…Waves propagating through earth media are routinely employed in seismic exploration. Many modern seismic imaging techniques require simulating seismic wave propagations repeatedly for numerous source terms and various instances of media parameters, cf., for instance, Symes (2009), Virieux and Operto (2009), Schuster (2014), and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Explicit coupling of acoustic and elastic wave propagation in finite-difference simulations

Gao

Keyes

2020

GEOPHYSICS

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We present a mechanism to explicitly couple the finite-difference discretizations of 2D acoustic and isotropic elastic wave systems that are separated by straight interfaces. Such coupled simulations allow the application of the elastic model to geological regions that are of special interest for seismic exploration studies (e.g., the areas surrounding salt bodies), while with the computationally more tractable acoustic model still being applied in the background regions. Specifically, the acoustic wave system is expressed in terms of velocity and pressure while the elastic wave system is expressed in terms of velocity and stress. Both systems are posed in first-order forms and discretized on staggered grids. Special variants of the standard finite-difference operators, namely, operators that possess the summation-by-parts property, are used for the approximation of spatial derivatives. Penalty terms, which are also referred to as the simultaneous approximation terms, are designed to weakly impose the elastic-acoustic interface conditions in the finite-difference discretizations and couple the elastic and acoustic wave simulations together. With the presented mechanism, we are able to perform the coupled elastic-acoustic wave simulations stably and accurately. Moreover, it is shown that the energy-conserving property in the continuous systems can be preserved in the discretization with carefully designed penalty terms.

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Seismic Imaging, Overview

Cited by 2 publications

References 86 publications

Seismic Image Identification and Detection Based on Tchebichef Moment Invariant

Seismic Image Identification and Detection Based on Tchebichef Moment Invariant

Explicit coupling of acoustic and elastic wave propagation in finite-difference simulations

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