Efficient seismic denoising techniques using robust principal component analysis

No abstract is available for this paper.

• Publication year

  2019

• Venue

  SEG technical program expanded abstracts

• Publication date

  2019-09-25

• Fields of study

  Computer Science, Engineering, Geology

• Identifiers
  DOI 10.1190/SEGAM2019-3216379.1
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Efficient Optimization Algorithms for Robust Principal Component Analysis and Its Variants

  2018cited by this paper
• A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

  2018cited by this paper
• Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis

  2017cited by this paper
• Non-convex Penalties with Analytical Solutions for One-bit Compressive Sensing

  2017cited by this paper
• Empirical Low-Rank Approximation for Seismic Noise Attenuation

  2017cited by this paper
• Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

  2015cited by this paper
• Interpolation and denoising of high-dimensional seismic data by learning a tight frame

  2015cited by this paper
• Double Sparsity Dictionary for Seismic Noise Attenuation

  2015cited by this paper
• An efficient finite-difference method with high-order accuracy in both time and space domains for modelling scalar-wave propagation

  2014cited by this paper
• Multiples: Signal or noise?

  2014cited by this paper
• Optimized first-order methods for smooth convex minimization

  2014cited by this paper
• Seismic data analysis using local time‐frequency decomposition

  2013cited by this paper
• Sparse and low-rank matrix decomposition via alternating direction method

  2013cited by this paper
• A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation

  2012cited by this paper
• Gradient methods for minimizing composite functions

  2012cited by this paper
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

  2011cited by this paper
• Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

  2010cited by this paper
• Seislet transform and seislet frame

  2010cited by this paper
• Nearly unbiased variable selection under minimax concave penalty

  2010cited by this paper
• Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

  2009cited by this paper
• Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

  2009cited by this paper
• Robust principal component analysis?

  2009cited by this paper
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

  2009cited by this paper
• Predictive removal of scattered noise

  2006cited by this paper
• Smooth minimization of non-smooth functions

  2005cited by this paper

• Unsupervised Intense VSP Coupling Noise Suppression With Iterative Robust Deep Learning

  2024cites this paper
• Unsupervised Erratic Seismic Noise Attenuation With Robust Deep Convolutional Autoencoders

  2022cites this paper
• Fast algorithms for robust principal component analysis with an upper bound on the rank

  2020cites this paper
• Manifold Denoising by Nonlinear Robust Principal Component Analysis

  2019cites this paper