A practical data-driven optimization strategy for Gaussian beam migration

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. S81-S92 ◽  
Author(s):  
Jidong Yang ◽  
Hejun Zhu
Keyword(s):  
Gaussian Beam ◽  
Signal To Noise Ratio ◽  
Data Driven ◽  
Control Factor ◽  
Optimization Strategy ◽  
Coherent Signals ◽  
Attribute Analysis ◽  
Imaging Results ◽  
Beam Center ◽  
Gaussian Beam Migration

Gaussian beam migration (GBM) is an efficient and accurate depth imaging technique, which allows us to resolve steep-dip structures and image multiple arrivals. Similar to Kirchhoff migration, GBM projects reflection events into the subsurface along traveltime isochrons. For data with low folds or signal-to-noise ratio (S/N), it produces migration artifacts, making it difficult for subsequent interpretation and attribute analysis. We have developed a data-driven optimization strategy to solve this problem. First, at the source and beam center locations, we estimate the instantaneous emergence angles of specular rays using semblance analysis for local common-shot and common-receiver gathers. Then, a quality control factor is designed to enhance the imaging results of coherent signals around the specular rays. Synthetic and field data examples demonstrate that our optimization strategy enables us to improve the imaging quality of GBM, especially for sparsely acquired and low-S/N data.

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

Gaussian beam migration

Geophysics ◽  
1990 ◽  
Vol 55 (11) ◽  
pp. 1416-1428 ◽  
Author(s):  
N. Ross Hill
Keyword(s):  
Gaussian Beam ◽  
Wave Field ◽  
Seismic Source ◽  
Velocity Model ◽  
Downward Continuation ◽  
Gaussian Beams ◽  
Seismic Velocities ◽  
Migration Method ◽  
Gaussian Beam Migration ◽  
Lateral Variations

Just as synthetic seismic data can be created by expressing the wave field radiating from a seismic source as a set of Gaussian beams, recorded data can be downward continued by expressing the recorded wave field as a set of Gaussian beams emerging at the earth’s surface. In both cases, the Gaussian beam description of the seismic‐wave propagation can be advantageous when there are lateral variations in the seismic velocities. Gaussian‐beam downward continuation enables wave‐equation calculation of seismic propagation, while it retains the interpretive raypath description of this propagation. This paper describes a zero‐offset depth migration method that employs Gaussian beam downward continuation of the recorded wave field. The Gaussian‐beam migration method has advantages for imaging complex structures. Like finite‐difference migration, it is especially compatible with lateral variations in velocity, but Gaussian beam migration can image steeply dipping reflectors and will not produce unwanted reflections from structure in the velocity model. Unlike other raypath methods, Gaussian beam migration has guaranteed regular behavior at caustics and shadows. In addition, the method determines the beam spacing that ensures efficient, accurate calculations. The images produced by Gaussian beam migration are usually stable with respect to changes in beam parameters.

A New Optimization Strategy for Chemical Mechanical Polishing Process

2000 ◽  
Author(s):  
Gou-Jen Wang ◽  
Jau-Liang Chen ◽  
Ju-Yi Hwang
Keyword(s):  
Neural Network ◽  
Taguchi Method ◽  
Signal To Noise Ratio ◽  
Global Optimum ◽  
Optimum Process ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Two Stage ◽  
Chemical Mechanical Polishing Process ◽  
Trained Neural Network

Abstract In this paper, a systematic approach to achieve global optimum CMP process is carried out. In this new approach, orthogonal array technique adopted from the Taguchi method is used for efficient experiment design. The neural network (NN) technique is then applied to model the complex CMP process. Signal to Noise Ratio (S/N) Analysis (ANOVA) technique used in the conventional Taguchi method is also implemented to obtain the local optimum process parameters. Successively, the global optimum parameters are acquired in terms of the trained neural network. In order to increase the CMP throughput, a two-stage optimal strategy is also proposed. Experimental results demonstrate that the two-stage strategy can perform better then the original approach even though the polishing time is reduced by 1/6.

Refraction wavefield migration

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. Q27-Q37
Author(s):  
Yang Shen ◽  
Jie Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Surface Velocity ◽  
Data Set ◽  
Near Surface ◽  
Depth Migration ◽  
Reflection Data ◽  
Imaging Tool ◽  
Imaging Results ◽  
Migration Method ◽  
Single Method

Refraction methods are often applied to model and image near-surface velocity structures. However, near-surface imaging is very challenging, and no single method can resolve all of the land seismic problems across the world. In addition, deep interfaces are difficult to image from land reflection data due to the associated low signal-to-noise ratio. Following previous research, we have developed a refraction wavefield migration method for imaging shallow and deep interfaces via interferometry. Our method includes two steps: converting refractions into virtual reflection gathers and then applying a prestack depth migration method to produce interface images from the virtual reflection gathers. With a regular recording offset of approximately 3 km, this approach produces an image of a shallow interface within the top 1 km. If the recording offset is very long, the refractions may follow a deep path, and the result may reveal a deep interface. We determine several factors that affect the imaging results using synthetics. We also apply the novel method to one data set with regular recording offsets and another with far offsets; both cases produce sharp images, which are further verified by conventional reflection imaging. This method can be applied as a promising imaging tool when handling practical cases involving data with excessively weak or missing reflections but available refractions.

scholarly journals Target-oriented Gaussian beam migration using a modified ray tracing scheme

2019 ◽  
Vol 16 (6) ◽  
pp. 1301-1319 ◽  
Author(s):  
Rui Zhang ◽  
Jian-Ping Huang ◽  
Su-Bin Zhuang ◽  
Zhen-Chun Li
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Large Scale ◽  
High Efficiency ◽  
Complex Structure ◽  
Back Propagation ◽  
Imaging Method ◽  
Computation Efficiency ◽  
Simple Implementation ◽  
Gaussian Beam Migration

Abstract For large-scale 3D seismic data, target-oriented reservoir imaging is more attractive than conventional full-volume migration, in terms of computation efficiency. Gaussian beam migration (GBM) is one of the most robust depth imaging method, which not only keeps the advantages of ray methods, such as high efficiency and flexibility, but also allows us to solve caustics and multipathing problems. But conventional Gaussian beam migration requires slant stack for prestack data, and ray tracing from beam center location to subsurface, which is not easy to be directly applied for target-oriented imaging. In this paper, we modify the conventional Gaussian beam migration scheme, by shooting rays from subsurface image points to receivers to implement wavefield back-propagation. This modification helps us to achieve a better subsurface illumination in complex structure and allows simple implementation for target reservoir imaging. Significantly, compared with the wavefield-based GBM, our method does not reconstruct the subsurface snapshots, which has higher efficiency. But the proposed method is not as efficient as the conventional Gaussian beam migration. Synthetic and field data examples demonstrate the validity and the target-oriented imaging capability of our method.

scholarly journals Analysis of Linear Non-Destructive Testing and Evaluation Methods for Thin-Walled Structure Inspection Using Ultrasonic Array

Coatings ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 146
Author(s):  
Yang Li ◽  
Zhenggan Zhou ◽  
Jun Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Near Field ◽  
Central Frequency ◽  
Destructive Testing ◽  
Array Transducer ◽  
Imaging Results ◽  
Ultrasonic Array ◽  
Thin Walled ◽  
Aluminum Plates ◽  
Non Destructive

The ultrasonic array used for thin-walled structure non-destructive inspection usually has a high central frequency so that the thickness-to-acoustic wavelength ratio is greater than 10. When the ratio is much smaller than 10, the reliability of the conventional ultrasonic array method will dramatically decrease due to the influence of the acoustic near-field. This situation is unavoidable since the available central frequency of the array transducer cannot be an arbitrarily large value. To optimize the inspection performance in this case, the testing of an ultrasonic array and the evaluation of a structure whose thickness is smaller than five-times the longitudinal wavelength are analyzed in this paper. Linear ultrasonic array methods using different combinations of wave patterns, reflection times, and coupling conditions are uniformly expressed as full matrix algorithms. Simulated and experimental full matrices of 6 mm-thick aluminum plates using a 5-MHz array transducer are captured to analyze their imaging performances and sizing abilities with respect to various defects. Analyses show that the inspection results of the wedge coupling method have a much higher signal-to-noise ratio (SNR) than the results of conventional direct contact methods. Circular defects and rectangular defects can be distinguished by comparing the imaging results of different modes. For the simulated circular defect, the diameter can be measured according to the maximum image amplitude of the defect. To simulate a rectangular defect located in the lower half of the region, the nominal length can be measured using a linear function whose input is a −6 dB drop in length of the SS-S mode image. For a real sample, the material anisotropy and complex self-reflections will decrease the SNR by about 10 dB.

An efficient ray-tracing method and its application to Gaussian beam migration in complex multilayered anisotropic media

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. T281-T289 ◽  
Author(s):  
Qianru Xu ◽  
Weijian Mao
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Initial Conditions ◽  
Anisotropic Media ◽  
Memory Storage ◽  
Ray Tracing Method ◽  
Embedded Discontinuities ◽  
Model Parameterization ◽  
Gaussian Beam Migration ◽  
Tracing Method

We have developed a fast ray-tracing method for multiple layered inhomogeneous anisotropic media, based on the generalized Snell’s law. Realistic geologic structures continuously varying with embedded discontinuities are parameterized by adopting cubic B-splines with nonuniformly spaced nodes. Because the anisotropic characteristic is often closely related to the interface configuration, this model parameterization scheme containing the natural inclination of the corresponding layer is particularly suitable for tilted transverse isotropic models whose symmetry axis is generally perpendicular to the direction of the layers. With this model parameterization, the first- and second-order spatial derivatives of the velocity within the interfaces can be effectively obtained, which facilitates the amplitude computation in dynamic ray tracing. By using complex initial conditions for the dynamic ray system and taking the multipath effect into consideration, our method is applicable to Gaussian beam migration. Numerical experiments of our method have been used to verify its effectiveness, practicability, and efficiency in memory storage and computation.

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