A Robust Adaptive Rank-Reduction Method for 3D Diffraction Separation and Imaging

2021 ◽  
Author(s):  
Peng Lin ◽  
Jingtao Zhao ◽  
Suping Peng ◽  
Xiaoqin Cui ◽  
Chuangjian Li ◽  
...  
Keyword(s):  
Reduction Method ◽  
Rank Reduction ◽  
Robust Adaptive

scholarly journals Obtaining free USArray data by multi-dimensional seismic reconstruction

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Yangkang Chen ◽  
Min Bai ◽  
Yunfeng Chen
Keyword(s):  
Incomplete Data ◽  
Reduction Method ◽  
Three Dimensional ◽  
Noisy Data ◽  
Rank Reduction ◽  
Uniform Sampling ◽  
Structure And Dynamics ◽  
Seismic Wavefield ◽  
Suppress Noise ◽  
Spatial Coherency

Abstract USArray, a pioneering project for the dense acquisition of earthquake data, provides a semi-uniform sampling of the seismic wavefield beneath its footprint and greatly advances the understanding of the structure and dynamics of Earth. Despite continuing efforts in improving the acquisition design, network irregularity still causes spatial sampling alias and incomplete, noisy data, which imposes major challenges in array-based data analysis and seismic imaging. Here we employ an iterative rank-reduction method to simultaneously reconstruct the missing traces and suppress noise, i.e., obtaining free USArray recordings as well as enhancing the existing data. This method exploits the spatial coherency of three-dimensional data and recovers the missing elements via the principal components of the incomplete data. We examine its merits using simulated and real teleseismic earthquake recordings. The reconstructed P wavefield enhances the spatial coherency and accuracy of tomographic travel time measurements, which demonstrates great potential to benefit seismic investigations based on array techniques.

Damped rank-reduction method for simultaneous denoising and reconstruction of 5D seismic data

2016 ◽  
Author(s):  
Yangkang Chen ◽  
Dong Zhang ◽  
Weilin Huang ◽  
Shaohuan Zu ◽  
Zhaoyu Jin ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Rank Reduction

A fast rank reduction method for f — x — y noise attenuation

2012 ◽  
Author(s):  
Jianjun Gao ◽  
David C. Bonar ◽  
Mauricio D. Sacchi ◽  
Zhipeng Liu
Keyword(s):  
Reduction Method ◽  
Noise Attenuation ◽  
Rank Reduction

3D diffraction separation and imaging using an adaptive rank-reduction method

2019 ◽  
Author(s):  
Peng Lin ◽  
Suping Peng ◽  
Rushan Wu ◽  
Jingtao Zhao ◽  
Xiaoqin Cui ◽  
...  
Keyword(s):  
Reduction Method ◽  
Rank Reduction

scholarly journals Five-dimensional seismic data reconstruction using the optimally damped rank-reduction method

2019 ◽  
Vol 218 (1) ◽  
pp. 224-246 ◽  
Author(s):  
Yangkang Chen ◽  
Min Bai ◽  
Zhe Guan ◽  
Qingchen Zhang ◽  
Mi Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Data Reconstruction ◽  
Rank Reduction ◽  
Seismic Data Reconstruction

Separation and imaging of seismic diffractions using a localized rank-reduction method with adaptively selected ranks

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. V497-V506
Author(s):  
Hang Wang ◽  
Xingye Liu ◽  
Yangkang Chen
Keyword(s):  
Plane Wave ◽  
Reduction Method ◽  
Seismic Profile ◽  
Low Rank ◽  
Imaging Method ◽  
Seismic Profiles ◽  
Rank Reduction ◽  
Diffraction Profile ◽  
Imaging Result ◽  
Accurate Performance

Seismic diffractions are weak seismic events hidden within the more dominant reflection events in a seismic profile. Separating diffraction energy from the poststack seismic profiles can help infer the subsurface discontinuities that generate the diffraction events. The separated seismic diffractions can be migrated with a traditional seismic imaging method or a specifically designed migration method to highlight the diffractors, that is, the diffraction image. Traditional diffraction separation methods based on the underlying plane-wave assumption are limited by either the inaccurate slope estimation or the plane-wave assumption of the plane-wave destruction filter and thus will cause reflection leakage into the separated diffraction profile. The leaked reflection energy will deteriorate the resolution of the subsequent diffraction imaging result. We have adopted a new diffraction separation method based on a localized rank-reduction (LRR) method. The LRR method assumes the reflection events to be locally low-rank and the diffraction energy can be separated by a rank-reduction operation. Compared to the global rank-reduction method, the LRR method is more constrained in selecting the rank and is free of separation artifacts. We use a carefully designed synthetic example to demonstrate that the LRR method can help separate the diffraction energy from a poststack seismic profile with kinematically and dynamically accurate performance.

Effective diffraction separation using the improved optimal rank-reduction method

Geophysics ◽  
2022 ◽  
pp. 1-85
Author(s):  
Peng Lin ◽  
Suping Peng ◽  
Xiaoqin Cui ◽  
Wenfeng Du ◽  
Chuangjian Li
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Singular Values ◽  
Vital Role ◽  
Singular Value ◽  
Low Rank ◽  
Small Scale ◽  
Frobenius Norm ◽  
Rank Reduction ◽  
Reduction Methods

Seismic diffractions encoding subsurface small-scale geologic structures have great potential for high-resolution imaging of subwavelength information. Diffraction separation from the dominant reflected wavefields still plays a vital role because of the weak energy characteristics of the diffractions. Traditional rank-reduction methods based on the low-rank assumption of reflection events have been commonly used for diffraction separation. However, these methods using truncated singular-value decomposition (TSVD) suffer from the problem of reflection-rank selection by singular-value spectrum analysis, especially for complicated seismic data. In addition, the separation problem for the tangent wavefields of reflections and diffractions is challenging. To alleviate these limitations, we propose an effective diffraction separation strategy using an improved optimal rank-reduction method to remove the dependence on the reflection rank and improve the quality of separation results. The improved rank-reduction method adaptively determines the optimal singular values from the input signals by directly solving an optimization problem that minimizes the Frobenius-norm difference between the estimated and exact reflections instead of the TSVD operation. This improved method can effectively overcome the problem of reflection-rank estimation in the global and local rank-reduction methods and adjusts to the diversity and complexity of seismic data. The adaptive data-driven algorithms show good performance in terms of the trade-off between high-quality diffraction separation and reflection suppression for the optimal rank-reduction operation. Applications of the proposed strategy to synthetic and field examples demonstrate the superiority of diffraction separation in detecting and revealing subsurface small-scale geologic discontinuities and inhomogeneities.

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