Pre-stack diffraction separation by parameterizing the reflection local slope

Geophysics ◽  
2021 ◽  
pp. 1-49
Author(s):  
Chuangjian Li ◽  
Suping Peng ◽  
Xiaoqin Cui ◽  
Qiannan Liu ◽  
Peng Lin
Keyword(s):  
Plane Wave ◽  
Separation Method ◽  
Small Scale ◽  
Reflected Waves ◽  
Local Slope ◽  
Illumination Direction ◽  
Novel Approach ◽  
Diffracted Waves ◽  
Slope Estimation ◽  
Ray Parameter

Diffracted waves provide the opportunity to detect small-scale subsurface structures because they give wide illumination direction of geological discontinuities such as faults, pinch-outs, and collapsed columns. However, separating diffracted waves is challenging because diffracted waves have greater geometrical amplitude losses and are generally weaker than reflections. To retain more diffracted waves, a pre-stack diffraction separation method is proposed based on the local slope pattern and plane-wave destruction method. Generally, it is difficult to distinguish between the hyperbolic reflections and hyperbolic diffractions using the data-driven local slope estimation in the shot domain. Therefore, we transfer the slope estimation in the shot domain to the velocity analysis in the common midpoint domain and the ray parameter calculation in the stack domain. The connection between the local slope and the normal move-out velocity and the surface-ray parameter is known, which provides a novel approach for estimating the local slope of the hyperbolic reflected waves in the shot domain. The estimated slope can provide an exact slope-based operator for the plane-wave destruction (PWD) method, thus allowing the PWD to separate diffracted waves from reflected waves in the shot domain. Synthetic and field data tests demonstrate the feasibility and effectiveness of the proposed pre-stack diffraction separation method.

Accelerated plane-wave destruction

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. V1-V9 ◽  
Author(s):  
Zhonghuan Chen ◽  
Sergey Fomel ◽  
Wenkai Lu
Keyword(s):  
Plane Wave ◽  
Iterative Algorithm ◽  
Field Experiments ◽  
Computation Time ◽  
Computational Time ◽  
Iterative Estimation ◽  
Local Slope ◽  
Slope Estimation ◽  
Correct Estimation ◽  
Wave Filter

When plane-wave destruction (PWD) is implemented by implicit finite differences, the local slope is estimated by an iterative algorithm. We propose an analytical estimator of the local slope that is based on convergence analysis of the iterative algorithm. Using the analytical estimator, we design a noniterative method to estimate slopes by a three-point PWD filter. Compared with the iterative estimation, the proposed method needs only one regularization step, which reduces computation time significantly. With directional decoupling of the plane-wave filter, the proposed algorithm is also applicable to 3D slope estimation. We present synthetic and field experiments to demonstrate that the proposed algorithm can yield a correct estimation result with shorter computational time.

Non-stationary local slope estimation via forward-backward space derivative calculation

Geophysics ◽  
2021 ◽  
pp. 1-91
Author(s):  
Hang Wang ◽  
Liuqing Yang ◽  
Xingye Liu ◽  
Yangkang Chen ◽  
Wei Chen
Keyword(s):  
Finite Difference ◽  
Random Noise ◽  
Estimation Method ◽  
Order Derivative ◽  
Space Domain ◽  
Local Slope ◽  
First Order ◽  
Slope Estimation ◽  
Difference Calculation ◽  
Seismic Images

The local slope estimated from seismic images has a variety of meaningful applications. Slope estimation based on the plane-wave destruction (PWD) method is one of the widely accepted techniques in the seismic community. However, the PWD method suffers from its sensitivity to noise in the seismic data. We propose an improved slope estimation method based on the PWD theory that is more robust in the presence of strong random noise. The PWD operator derived in the Z-transform domain contains a phase-shift operator in space corresponding to the calculation of the first-order derivative of the wavefield in the space domain. The first-order derivative is discretized based on a forward finite difference in the traditional PWD method, which lacks the constraint from the backward direction. We propose an improved method by discretizing the first-order space derivative based on an averaged forward-backward finite-difference calculation. The forward-backward space derivative calculation makes the space-domain first-order derivative more accurate and better anti-noise since it takes more space grids for the derivative calculation. In addition, we introduce non-stationary smoothing to regularize the slope estimation and to make it even more robust to noise. We demonstrate the performance of the new slope estimation method by several synthetic and field data examples in different applications, including 2D/3D structural filtering, structure-oriented deblending, and horizon tracking.

scholarly journals Optimisation of confinement in a fusion reactor using a nonlinear turbulence model

2018 ◽  
Vol 84 (2) ◽  
Author(s):  
E. G. Highcock ◽  
N. R. Mandell ◽  
M. Barnes ◽  
W. Dorland
Keyword(s):  
First Principles ◽  
Figure Of Merit ◽  
Large Scale ◽  
Unit Volume ◽  
Fusion Reactor ◽  
Small Scale ◽  
Zonal Flows ◽  
Novel Approach ◽  
First Time ◽  
Turbulent Cascade

The confinement of heat in the core of a magnetic fusion reactor is optimised using a multidimensional optimisation algorithm. For the first time in such a study, the loss of heat due to turbulence is modelled at every stage using first-principles nonlinear simulations which accurately capture the turbulent cascade and large-scale zonal flows. The simulations utilise a novel approach, with gyrofluid treatment of the small-scale drift waves and gyrokinetic treatment of the large-scale zonal flows. A simple near-circular equilibrium with standard parameters is chosen as the initial condition. The figure of merit, fusion power per unit volume, is calculated, and then two control parameters, the elongation and triangularity of the outer flux surface, are varied, with the algorithm seeking to optimise the chosen figure of merit. A twofold increase in the plasma power per unit volume is achieved by moving to higher elongation and strongly negative triangularity.

scholarly journals Automating XFEM Modeling Process for Optimal Failure Predictions

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
A. Y. Elruby ◽  
Sam Nakhla ◽  
A. Hussein
Keyword(s):  
Finite Element ◽  
Test Data ◽  
Critical Region ◽  
Computational Effort ◽  
Small Scale ◽  
Extended Finite Element ◽  
Novel Approach ◽  
Python Scripting ◽  
Failure Loads ◽  
Failure Predictions

The eXtended Finite Element Method (XFEM) is a versatile method for solving crack propagation problems. Meanwhile, XFEM predictions for crack onset and propagation rely on the stress field which tends to converge at a slower rate than that of displacements, making it challenging to capture critical load at crack onset accurately. Furthermore, identifying the critical region(s) for XFEM nodal enrichments is user-dependent. The identification process can be straightforward for small-scale test specimen while in other cases such as complex structures it can be unmanageable. In this work a novel approach is proposed with three major objectives; (1) alleviate user-dependency; (2) enhance predictions accuracy; (3) minimize computational effort. An automatic critical region(s) identification based on material selected failure criterion is developed. Moreover, the approach enables the selection of optimized mesh necessary for accurate prediction of failure loads at crack initiation. Also, optimal enrichment zone size determination is automated. The proposed approach was developed as an iterative algorithm and implemented in ABAQUS using Python scripting. The proposed algorithm was validated against our test data of unnotched specimens and relevant test data from the literature. The results of the predicted loads/displacements at failure are in excellent agreement with measurements. Crack onset locations were in very good agreement with observations from testing. Finally, the proposed algorithm has shown a significant enhancement in the overall computational efficiency compared to the conventional XFEM. The proposed algorithm can be easily implemented into user-built or commercial finite element codes.

scholarly journals Separation of diffracted waves via SVD filter

2020 ◽  
Vol 17 (5) ◽  
pp. 1259-1271
Author(s):  
Hong-Yan Shen ◽  
Qin Li ◽  
Yue-Ying Yan ◽  
Xin-Xin Li ◽  
Jing Zhao
Keyword(s):  
Seismic Waves ◽  
Original Data ◽  
Identification Accuracy ◽  
Reflected Waves ◽  
Wave Separation ◽  
Imaging Results ◽  
Diffracted Waves ◽  
Fast Processing ◽  
The Difference ◽  
Value Decomposition

Abstract Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones. However, the energy of diffracted waves is weaker than that of reflections. Therefore, the extraction of diffracted waves is the basis for the effective utilization of diffracted waves. Based on the difference in travel times between diffracted and reflected waves, we developed a method for separating the diffracted waves via singular value decomposition filters and presented an effective processing flowchart for diffracted wave separation and imaging. The research results show that the horizontally coherent difference between the reflected and diffracted waves can be further improved using normal move-out (NMO) correction. Then, a band-rank or high-rank approximation is used to suppress the reflected waves with better transverse coherence. Following, separation of reflected and diffracted waves is achieved after the filtered data are transformed into the original data domain by inverse NMO. Synthetic and field examples show that our proposed method has the advantages of fewer constraints, fast processing speed and complete extraction of diffracted waves. And the diffracted wave imaging results can effectively improve the identification accuracy of geological heterogeneous bodies or zones.

scholarly journals Differentiable Reasoning on Large Knowledge Bases and Natural Language

2020 ◽  
Vol 34 (04) ◽  
pp. 5182-5190
Author(s):  
Pasquale Minervini ◽  
Matko Bošnjak ◽  
Tim Rocktäschel ◽  
Sebastian Riedel ◽  
Edward Grefenstette
Keyword(s):  
Natural Language ◽  
Link Prediction ◽  
Question Answering ◽  
Knowledge Bases ◽  
Small Scale ◽  
Reasoning Systems ◽  
Novel Approach ◽  
Real World Datasets ◽  
Interpretable Models ◽  
Machine Reading

Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data-inefficient, and it is hard to analyse their reasoning process. These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models 1. Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models.

Adsorption Materials Development for the Separation of Actinides and Specific Fission Products from High Level Waste

2014 ◽  
Vol 94 ◽  
pp. 103-110 ◽  
Author(s):  
Yue Zhou Wei ◽  
Shun Yan Ning ◽  
Qi Long Wang ◽  
Zi Chen ◽  
Yan Wu ◽  
...  
Keyword(s):  
Spent Nuclear Fuel ◽  
Selective Adsorption ◽  
Packed Column ◽  
Liquid Waste ◽  
Separation Method ◽  
High Level Waste ◽  
Small Scale ◽  
Fast Diffusion ◽  
High Level Liquid Waste ◽  
High Level

The long-term radiotoxicity of high level liquid waste (HLLW) generated in spent nuclear fuel reprocessing is governed by the content of several long-lived minor actinides (MA) and some specific fission product nuclides. To efficiently separate MA (Am, Cm) and some FPs such as Cs and Sr from the HLLW, we have been studying an advanced aqueous partitioning process, which uses selective adsorption as separation method. In this work, we prepared different types of porous silica-based organic/inorganic adsorbents with fast diffusion kinetics, improved chemical stability and low pressure drop in a packed column. So they are advantageously applicable to efficient separation of the MA and specific FP elements from HLLW. Adsorption and separation behaviors of the MA and some FP elements such as Cs and Sr were studied. Small scale separation tests using simulated and genuine nuclear waste solutions were carried out and the obtained results indicate that the proposed separation method based on selective adsorption is essentially feasible.

Poststack velocity analysis by separation and imaging of seismic diffractions

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. U89-U94 ◽  
Author(s):  
Sergey Fomel ◽  
Evgeny Landa ◽  
M. Turhan Taner
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Field Data ◽  
Small Scale ◽  
Velocity Analysis ◽  
High Resolution Imaging ◽  
Diffracted Waves ◽  
Resolution Imaging ◽  
Seismic Reflections ◽  
Optimal Focusing

Small geologic features manifest themselves in seismic data in the form of diffracted waves, which are fundamentally different from seismic reflections. Using two field-data examples and one synthetic example, we demonstrate the possibility of separating seismic diffractions in the data and imaging them with optimally chosen migration velocities. Our criteria for separating reflection and diffraction events are the smoothness and continuity of local event slopes that correspond to reflection events. For optimal focusing, we develop the local varimax measure. The objectives of this work are velocity analysis implemented in the poststack domain and high-resolution imaging of small-scale heterogeneities. Our examples demonstrate the effectiveness of the proposed method for high-resolution imaging of such geologic features as faults, channels, and salt boundaries.

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