Diffraction Imaging in Depth

Moser, Tijmen Jan; Howard, C. B.

doi:10.3997/2214-4609.201401853

Cited by 33 publications

(56 citation statements)

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“…The offset‐midpoint travel‐time pyramid (29) denotes the P‐wave diffraction travel time from a single scattering point in a 2D TTI medium. Diffraction travel time is used for diffraction‐based seismic imaging (e.g., Moser and Howard ; Waheed et al . ) and local velocity analysis (e.g., Dell et al .…”

Section: Discussionmentioning

confidence: 99%

The offset‐midpoint travel‐time pyramid for P‐wave in 2D transversely isotropic media with a tilted symmetry axis

Hao

Stovas

2014

Geophysical Prospecting

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For pre‐stack phase‐shift migration in homogeneous isotropic media, the offset‐midpoint travel time is represented by the double‐square‐root equation. The travel time as a function of offset and midpoint resembles the shape of Cheops’ pyramid. This is also valid for transversely isotropic media with a vertical symmetry axis. In this study, we extend the offset‐midpoint travel‐time pyramid to the case of 2D transversely isotropic media with a tilted symmetry axis. The P‐wave analytical travel‐time pyramid is derived under the assumption of weak anelliptical property of the tilted transverse isotropy media. The travel‐time equation for the dip‐constrained transversely isotropic model is obtained from the depth‐domain travel‐time pyramid. The potential applications of the derived offset‐midpoint travel‐time equation include pre‐stack Kirchhoff migration, anisotropic parameter estimation, and travel‐time calculation in transversely isotropic media with a tilted symmetry axis.

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Section: Discussionmentioning

confidence: 99%

The offset‐midpoint travel‐time pyramid for P‐wave in 2D transversely isotropic media with a tilted symmetry axis

Hao

Stovas

2014

Geophysical Prospecting

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“…The algorithm we used in our study is working in the local angle domain, treating every node of the imaging grid as a point scatterer (Audebert et al . ; Moser and Howard ; Merten and Ettrich ). In the following, we explain the procedure in more detail without accounting for implementation performance specificities.…”

Section: Methodsmentioning

confidence: 99%

“…Yet these objects will provoke the scattering of waves in all directions, a phenomenon called diffraction. Due to their truncated Fresnel zone, diffracted waves contain a higher resolution signal and are valuable for an enhanced interpretation and inversion (Khaidukov, Landa and Moser ; Moser and Howard ; Landa, Fomel and Reshef ; Huang, Zhang and Schuster ). However, diffracted signal is not often available to the interpreter because its amplitude can be far weaker than reflected signal and is often lost during processing (Khaidukov et al .…”

Section: Introductionmentioning

confidence: 99%

Detection of point scatterers using diffraction imaging and deep learning

Tschannen

Ettrich²,

Delescluse

et al. 2019

Geophysical Prospecting

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A B S T R A C TDiffracted waves carry high-resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high-accuracy and high-resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.

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“…; Alimoradi et al . ; Moser and Howard ; Sénéchal G., Rousset, and Gaffet ). Each prediction technique is based on the presence of contrast in specific physical properties of the surrounding rock, e.g., the dielectric constant and the acoustic impedance (Grodner ; Schrott and Sass ; Yaramanci ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of tunneling hazardous geological zones using the active seismic approach

Jiao

Tian

Liu

et al. 2015

Near Surface Geophysics

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This paper presents a seismic method and a newly developed device to predict the hazardous geological zones ahead of a tunnel face. The equipment used to predict hazardous geological zones consists of three components: the seismic source, the acquisition unit, and the data processing unit. The following features enable this device to be a valuable tool in predicting hazardous geological zones: the precise trigger time detection; the application of semi‐liquid couplant, which improves geophone coupling with the surrounding rock; the filtering of the prominent tunnel acoustic wave interference; and a software package of information on hazardous geological zones. Through extensive detection exercises of hazardous geological zones in the excavations of nearly 100 tunnel projects in China, it has been demonstrated that this device can satisfactorily predict hazardous geological zones, such as faults, water‐bearing zones, karst caverns, etc., within 150 m in front of the tunnel faces.

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Diffraction Imaging in Depth

Cited by 33 publications

References 0 publications

The offset‐midpoint travel‐time pyramid for P‐wave in 2D transversely isotropic media with a tilted symmetry axis

The offset‐midpoint travel‐time pyramid for P‐wave in 2D transversely isotropic media with a tilted symmetry axis

Detection of point scatterers using diffraction imaging and deep learning

Prediction of tunneling hazardous geological zones using the active seismic approach

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