Joint deblending and data reconstruction with focal transformation

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. V219-V231 ◽  
Author(s):  
Junhai Cao ◽  
Eric Verschuur ◽  
Hanming Gu ◽  
Lie Li
Keyword(s):  
Incomplete Data ◽  
Field Data ◽  
Random Noise ◽  
Recovery Process ◽  
Negative Influence ◽  
Seismic Exploration ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Blended Data ◽  
Simultaneous Source

Blended or simultaneous source shooting is becoming more widely used in seismic exploration and monitoring, which can provide significant uplift in terms of acquisition quality and economic efficiency. Effective deblending techniques are essential to make use of existing processing and imaging methodologies. When dealing with coarse and/or irregularly sampled blended data, the aliasing noise of incomplete data will affect the deblending process and the crosstalk in the blended data will also have a negative influence on the process of data reconstruction. Thus, we have developed a joint deblending and data-reconstruction method using the double-focal transformation to eliminate blending noise and aliasing noise in the coarse, blended data. Numerically blended synthetic and field-data examples demonstrate the validity of its application for deblending and data reconstruction. We also investigate the effect of random noise on the recovery process, and it shows that the algorithm would obtain optimum results after applying a denoising process before deblending and data reconstruction.

scholarly journals Deblending by direct inversion

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. A9-A12 ◽  
Author(s):  
Kees Wapenaar ◽  
Joost van der Neut ◽  
Jan Thorbecke
Keyword(s):  
Inverse Problem ◽  
Seismic Data ◽  
Iterative Procedure ◽  
Inversion Process ◽  
Direct Inversion ◽  
Source Data ◽  
Band Limited ◽  
Blended Data ◽  
Simultaneous Source ◽  
Additional Constraints

Deblending of simultaneous-source data is usually considered to be an underdetermined inverse problem, which can be solved by an iterative procedure, assuming additional constraints like sparsity and coherency. By exploiting the fact that seismic data are spatially band-limited, deblending of densely sampled sources can be carried out as a direct inversion process without imposing these constraints. We applied the method with numerically modeled data and it suppressed the crosstalk well, when the blended data consisted of responses to adjacent, densely sampled sources.

S *

1984 ◽  
Vol 74 (1) ◽  
pp. 61-78
Author(s):  
P. R. Gutowski ◽  
F. Hron ◽  
D. E. Wagner ◽  
S. Treitel
Keyword(s):  
Field Data ◽  
Shear Waves ◽  
Grazing Incidence ◽  
Point Sources ◽  
Seismic Exploration ◽  
Source Depth ◽  
Positive Maximum ◽  
Elastic Boundary ◽  
Vertical Changes ◽  
Common Situation

Abstract The interesting and common situation of shallow point sources close to the free surface can be modeled using an explicit elastic finite difference procedure. If the source depth is less than the predominant wavelength from the surface or other well-defined elastic boundary, then shear waves S*, are generated with an amplitude which depends strongly on the source depth. As the source depth is decreased, the amplitude of the resulting shear wave increases exponentially and the particle motion is confined to a plane perpendicular to the direction of propagation. These shear waves radiate from a point on the boundary directly above the source with a radiation pattern that is zero at grazing incidence, rises to a positive maximum at about 55° from the vertical, changes polarity, and increases negatively until merging with the PS wave. Since the use of shallow explosive sources for seismic exploration is favorable for the generation of S*, we present a field data case as an illustration.

Normalized shaping regularization for robust separation of blended data

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V281-V293 ◽  
Author(s):  
Qiang Zhao ◽  
Qizhen Du ◽  
Xufei Gong ◽  
Xiangyang Li ◽  
Liyun Fu ◽  
...  
Keyword(s):  
Cost Savings ◽  
High Amplitude ◽  
Data Consistency ◽  
Common Source ◽  
Ocean Bottom ◽  
Special Cases ◽  
Non Gaussian ◽  
Blended Data ◽  
Power Line Noise ◽  
Simultaneous Source

Simultaneous source acquisition has attracted more and more attention from geophysicists because of its cost savings, whereas it also brings some challenges that have never been addressed before. Deblending of simultaneous source data is usually considered as an underdetermined inverse problem, which can be effectively solved with a least-squares (LS) iterative procedure between data consistency ([Formula: see text]-norm) and regularization ([Formula: see text]-norm or [Formula: see text]-norm). However, when it comes to abnormal noise that follows non-Gaussian distribution and possesses high-amplitude features (e.g., erratic noise, swell noise, and power line noise), the [Formula: see text]-norm is a nonrobust statistic that can easily lead to suboptimal deblended results. Although abnormal noise can be attenuated in the common source domain at first, it is still challenging to apply a coherency-based filter due to the sparse receiver or crossline sampling, e.g., that commonly found in ocean bottom node (OBN) acquisition. To address this problem, we have developed a normalized shaping regularization to make the inversion-based deblending approach robust for the separation of blended data when abnormal noise exists. Its robustness comes from the normalized shaping operator defined by the confidence interval of normal distribution, which minimizes the abnormal risk to a normal level to satisfy the assumption of LS shaping regularization. In special cases, the proposed approach will revert to the classic LS shaping regularization once the normalized coefficient is large enough. Experimental results on synthetic and field data indicate that the proposed method can effectively restore the separated records from blended data at essentially the same convergence rate as the LS shaping regularization for the abnormal noise-free scenario, but it can obtain better deblending performance and less energy leakage when abnormal noise exists.

Regularization by Denoising for simultaneous source separation

Geophysics ◽  
2021 ◽  
pp. 1-56
Author(s):  
Breno Bahia ◽  
Rongzhi Lin ◽  
Mauricio Sacchi
Keyword(s):  
Inverse Problem ◽  
Inverse Problems ◽  
Data Processing ◽  
Spectrum Analysis ◽  
Numerical Experiments ◽  
Source Separation ◽  
Singular Spectrum Analysis ◽  
Source Data ◽  
Blended Data ◽  
Simultaneous Source

Denoisers can help solve inverse problems via a recently proposed framework known as regularization by denoising (RED). The RED approach defines the regularization term of the inverse problem via explicit denoising engines. Simultaneous source separation techniques, being themselves a combination of inversion and denoising methods, provide a formidable field to explore RED. We investigate the applicability of RED to simultaneous-source data processing and introduce a deblending algorithm named REDeblending (RDB). The formulation permits developing deblending algorithms where the user can select any denoising engine that satisfies RED conditions. Two popular denoisers are tested, but the method is not limited to them: frequency-wavenumber thresholding and singular spectrum analysis. We offer numerical blended data examples to showcase the performance of RDB via numerical experiments.

Flow field reconstruction method based on array neural network

2020 ◽  
Vol 125 (1283) ◽  
pp. 223-243
Author(s):  
W. Yuqi ◽  
Y. Wu ◽  
L. Shan ◽  
Z. Jian ◽  
R. Huiying ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Field ◽  
Traditional Method ◽  
Field Data ◽  
Reconstruction Method ◽  
Target Problem ◽  
Field Reconstruction ◽  
Flow Field Characteristics ◽  
Database Technology ◽  
Flow Field Reconstruction

ABSTRACTMulti-dimensional aerodynamic database technology is widely used, but its model often has the curse of dimensionality. In order to solve this problem, we need projection to reduce the dimension. In addition, due to the lack of traditional method, we have improved the traditional flow field reconstruction method based on artificial neural networks, and we proposed an array neural network method.In this paper, a set of flow field data for the target problem of the fixed Mach number is obtained by the existing CFD method. Then we arrange all the sampled flow field data into a matrix and use proper orthogonal decomposition (POD) to reduce the dimension, whose size is determined by the first few modals of energy. Therefore, significantly reduced data are obtained. Then we use an arrayed neural network to map the flow field data of simplified target problem and the flow field characteristics. Finally, the unknown flow field data can be effectively predicted through the flow field characteristic and the trained array neural network.At the end of this paper, the effectiveness of the method is verified by airfoil flow fields. The calculation results show that the array neural network can reconstruct the flow field of the target problem more accurately than the traditional method, and its convergence speed is significantly faster. In addition, for the case of high angle flow field, the array neural network also performs well. There are no obvious jumps, and huge errors are found in results. In general, the proposed method is better than the traditional method.

Gravity anomaly reconstruction based on nonequispaced Fourier transform

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. G83-G92
Author(s):  
Ya Xu ◽  
Fangzhou Nan ◽  
Weiping Cao ◽  
Song Huang ◽  
Tianyao Hao
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Gravity Data ◽  
Signal Reconstruction ◽  
Gaussian Function ◽  
Reconstruction Algorithm ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Grid Data ◽  
2D Data

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

scholarly journals Multi-Perspective Ultrasound Imaging Technology of the Breast with Cylindrical Motion of Linear Arrays

2019 ◽  
Vol 9 (3) ◽  
pp. 419 ◽  
Author(s):  
Chang Liu ◽  
Binzhen Zhang ◽  
Chenyang Xue ◽  
Wendong Zhang ◽  
Guojun Zhang ◽  
...  
Keyword(s):  
Chest Wall ◽  
Ultrasound Imaging ◽  
Ultrasonic Transducer ◽  
Absolute Error ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Water Tank ◽  
Volume Data ◽  
Imaging Technology ◽  
Linear Arrays

In this paper, we propose a multi-perspective ultrasound imaging technology with the cylindrical motion of four piezoelectric micromachined ultrasonic transducer (PMUT) rotatable linear arrays. The transducer is configured in a cross shape vertically on the circle with the length of the arrays parallel to the z axis, roughly perpendicular to the chest wall. The transducers surrounded the breast, which achieves non-invasive detection. The electric rotary table drives the PMUT to perform cylindrical scanning. A breast model with a 2 cm mass in the center and six 1-cm superficial masses were used for the experimental analysis. The detection was carried out in a water tank and the working temperature was constant at 32 °C. The breast volume data were acquired by rotating the probe 90° with a 2° interval, which were 256 × 180 A-scan lines. The optimized segmented dynamic focusing technology was used to improve the image quality and data reconstruction was performed. A total of 256 A-scan lines at a constant angle were recombined and 180 A-scan lines were recombined according to the nth element as a dataset, respectively. Combined with ultrasound imaging algorithms, multi-perspective ultrasound imaging was realized including vertical slices, horizontal slices and 3D imaging. The seven masses were detected and the absolute error of the size was approximately 1 mm where even the image of the injection pinhole could be seen. Furthermore, the breast boundary could be seen clearly from the chest wall to the nipple, so the location of the masses was easier to confirm. Therefore, the validity and feasibility of the data reconstruction method and imaging algorithm were verified. It will be beneficial for doctors to be able to comprehensively observe the pathological tissue.

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