A Non-causal Inverse Model for Source Signal Recovery in Large-Domain Wave Propagation

2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hunter M. Brown ◽  
Minh Q. Phan ◽  
Stephen A. Ketcham
Keyword(s):  
Wave Propagation ◽  
Inverse Model ◽  
Signal Recovery ◽  
Large Domain ◽  
Source Signal

Compressed wavefield extrapolation

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. SM77-SM93 ◽  
Author(s):  
Tim T. Lin ◽  
Felix J. Herrmann
Keyword(s):  
Wave Propagation ◽  
Compressed Sensing ◽  
Signal Recovery ◽  
New Paradigm ◽  
New Approach ◽  
Measurement Basis ◽  
Recent Developments ◽  
Data Volume ◽  
Extrapolation Problem ◽  
Wavefield Extrapolation

An explicit algorithm for the extrapolation of one-way wavefields is proposed that combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3D. By using ideas from compressed sensing, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. Compressed sensing entails a new paradigm for signal recovery that provides conditions under which signals can be recovered from incomplete samplings by nonlinear recovery methods that promote sparsity of the to-be-recovered signal. According to this theory, signals can be successfully recovered when the measurement basis is incoherent with the representa-tion in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can be successfully extrapolated in the modal domain, despite evanescent wave modes. The degree to which the wavefield can be recovered depends on the number of missing (evanescent) wavemodes and on the complexity of the wavefield. A proof of principle for the compressed sensing method is given for inverse wavefield extrapolation in 2D, together with a pathway to 3D during which the multiscale and multiangular properties of curvelets, in relation to the Helmholz operator, are exploited. The results show that our method is stable, has reduced dip limitations, and handles evanescent waves in inverse extrapolation.

Superstable Models for Short-Duration Large-Domain Wave Propagation

2012 ◽  
pp. 257-269 ◽  
Author(s):  
Minh Q. Phan ◽  
Stephen A. Ketcham ◽  
Richard S. Darling ◽  
Harley H. Cudney
Keyword(s):  
Wave Propagation ◽  
Short Duration ◽  
Large Domain

scholarly journals Sparse Signal Recovery for Direction-of-Arrival Estimation Based on Source Signal Subspace

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Lin ◽  
Jiying Liu ◽  
Meihua Xie ◽  
Jubo Zhu
Keyword(s):  
Computational Cost ◽  
Real Data ◽  
Direction Of Arrival ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Signal Subspace ◽  
Second Order Cone ◽  
Source Signal ◽  
Improved Accuracy

After establishing the sparse representation of the source signal subspace, we propose a new method to estimate the direction of arrival (DOA) by solving anℓ1-norm minimization for sparse signal recovery of the source powers. Second-order cone programming is applied to reformulate this optimization problem, and it is solved effectively by employing the interior point method. Due to the keeping of the signal subspace and the discarding of the noise subspace, the proposed method is more robust to noise than many other sparsity-based methods. The real data tests and the numerical simulations demonstrate that the proposed method has improved accuracy and robustness to noise, and it is not sensitive to the knowledge about the number of sources. We discuss the computational cost of our method theoretically, and the experiment results verify the computational effectiveness.

State-space Modeling of Large Domain Wave Propagation Systems by Partitioned C-matrices

2013 ◽  
Vol 60 (3-4) ◽  
pp. 541-558
Author(s):  
Richard S. Darling ◽  
Minh Q. Phan ◽  
Stephen A. Ketcham
Keyword(s):  
Wave Propagation ◽  
State Space ◽  
Large Domain ◽  
Space Modeling ◽  
State Space Modeling

scholarly journals A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1677
Author(s):  
Qingyi Wang ◽  
Yiqiong Zhang ◽  
Shuai Yin ◽  
Yuduo Wang ◽  
Genping Wu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Signal Recovery ◽  
Subspace Projection ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Time Frequency ◽  
Source Signal ◽  
Mixing Matrix

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

A Lineariztion Approach for Blind Signal Recovery in Multipath MIMO Systems with Signal Misalignment

2020 ◽  
Author(s):  
Jiong Li
Keyword(s):  
Blind Deconvolution ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Taylor Series Expansion ◽  
Signal Recovery ◽  
Well Performance ◽  
Recovery Algorithm ◽  
Blind Signal ◽  
Input Multiple Output ◽  
Source Signal

Abstract This paper deals with blind deconvolution for signal recovery in multipath multiple-input multiple-output (MIMO) systems, where the delays of different paths of each source signal from transmit antenna to receive antenna are random. Such a problem is often solved in an ideal state in literature, i.e., each transmitted signal arrives at the receive antennas simultaneously and the arrival time intervals of two adjacent paths are identical. However, the ideal case could not be satisfied in most applications. To address this issue, we propose a blind signal recovery algorithm. Specifically, by using Taylor series expansion to approximate sources, the convolutive MIMO signal recovery problem is transferred into instantaneous blind source separation (BSS) problem. Building on the ideas of second-order blind identification (SOBI), an extended SOBI algorithm is developed to recover the extended sources (including original sources and their derivatives). The simulation results illustrate the well performance and the interest of the proposed algorithm in comparison with other approaches.

scholarly journals Speech Signal Recovery in Communication Networks

10.14311/542 ◽  
2004 ◽  
Vol 44 (2) ◽  
Author(s):  
V. Zagursky ◽  
A. Riekstinsh
Keyword(s):  
Communication Networks ◽  
Speech Signal ◽  
Standard Procedure ◽  
Second Order ◽  
Signal Recovery ◽  
Packet Losses ◽  
Communications Networks ◽  
Adaptive Interpolation ◽  
Source Signal ◽  
Correlation Properties

Interpolation approaches to the shape recovery of a speech signal in transmission over packet switched communications networks are proposed. The samples of signal fragments are mixed and transmitted in correspondence with standard procedure for packet-switched transmission. After reception a reverse permutation is made. In the case of packet losses missing samples are separated by several samples of the source signal. Correlation properties of the signal are used for the recovery samples due to first- and second-order non-adaptive and adaptive interpolation. For the loss of 25 % packets and second order adaptive interpolation a 2- 4 % error distribution range has been achieved.

Electron Microscopy of Shock Loaded Polycrystalline Beryllium

1974 ◽  
Vol 32 ◽  
pp. 506-507 ◽  
Author(s):  
J. M. Galbraith ◽  
L. E. Murr ◽  
A. L. Stevens
Keyword(s):  
Electron Microscopy ◽  
Wave Propagation ◽  
Structural Change ◽  
Single Crystal ◽  
Diffraction Pattern ◽  
Shock Loading ◽  
Slip Systems ◽  
Compression Tests ◽  
X Ray Diffraction ◽  
X Ray

Uniaxial compression tests and hydrostatic tests at pressures up to 27 kbars have been performed to determine operating slip systems in single crystal and polycrystal1ine beryllium. A recent study has been made of wave propagation in single crystal beryllium by shock loading to selectively activate various slip systems, and this has been followed by a study of wave propagation and spallation in textured, polycrystal1ine beryllium. An alteration in the X-ray diffraction pattern has been noted after shock loading, but this alteration has not yet been correlated with any structural change occurring during shock loading of polycrystal1ine beryllium.This study is being conducted in an effort to characterize the effects of shock loading on textured, polycrystal1ine beryllium. Samples were fabricated from a billet of Kawecki-Berylco hot pressed HP-10 beryllium.

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