scholarly journals Redshift of earthquakes via focused blind deconvolution of teleseisms

2020 ◽  
Vol 223 (3) ◽  
pp. 1864-1878
Author(s):  
Pawan Bharadwaj ◽  
Chunfang Meng ◽  
Aimé Fournier ◽  
Laurent Demanet ◽  
Mike Fehler
Keyword(s):  
Blind Deconvolution ◽  
Source Spectrum ◽  
Rupture Propagation ◽  
Source Characteristics ◽  
Eastern California Shear Zone ◽  
Source Signals ◽  
Source Extraction ◽  
Direction Opposite ◽  
Fraunhofer Approximation ◽  
High Frequency Content

SUMMARY We present a robust factorization of the teleseismic waveforms resulting from an earthquake source into signals that originate from the source and signals that characterize the path effects. The extracted source signals represent the earthquake spectrum, and its variation with azimuth. Unlike most prior work on source extraction, our method is data-driven, and it does not depend on any path-related assumptions, for example, the empirical Green’s function. Instead, our formulation involves focused blind deconvolution (FBD), which associates the source characteristics with the similarity among a multitude of recorded signals. We also introduce a new spectral attribute, to be called redshift, which is based on the Fraunhofer approximation. Redshift describes source-spectrum variation, where a decrease in high-frequency content occurs at the receiver in the direction opposite to unilateral rupture propagation. Using the redshift, we identified unilateral ruptures during two recent strike-slip earthquakes. The FBD analysis of an earthquake, which originated in the eastern California shear zone, is consistent with observations from local seismological or geodetic instrumentation.

Blind Deconvolution to Improve Classification of Transient Source Signals in Multipath

2000 ◽  
Author(s):  
Lisa A. Pflug ◽  
George B. Smith ◽  
Michael K. Broadhead
Keyword(s):  
Blind Deconvolution ◽  
Transient Source ◽  
Source Signals

Research on Blind Signal Extraction Based on Normalized Kurtosis

2014 ◽  
Vol 989-994 ◽  
pp. 3609-3612
Author(s):  
Yong Jian Zhao
Keyword(s):  
Biomedical Applications ◽  
Prior Information ◽  
Signal Extraction ◽  
Promising Technique ◽  
Blind Source Extraction ◽  
Blind Signal ◽  
Source Signal ◽  
Blind Signal Extraction ◽  
Source Signals ◽  
Source Extraction

Blind source extraction (BSE) is a promising technique to solve signal mixture problems while only one or a few source signals are desired. In biomedical applications, one often knows certain prior information about a desired source signal in advance. In this paper, we explore specific prior information as a constrained condition so as to develop a flexible BSE algorithm. One can extract a desired source signal while its normalized kurtosis range is known in advance. Computer simulations on biomedical signals confirm the validity of the proposed algorithm.

Blind deconvolution of remote‐source signals from ocean acoustic array recordings.

2010 ◽  
Vol 127 (3) ◽  
pp. 1963-1963
Author(s):  
Shima H. Abadi ◽  
David R. Dowling ◽  
Daniel Rouseff
Keyword(s):  
Blind Deconvolution ◽  
Acoustic Array ◽  
Source Signals ◽  
Array Recordings

BEARING COMPOUND FAULT ACOUSTIC DIAGNOSIS BASED ON IMPROVED BLIND DECONVOLUTION ALGORITHM

2015 ◽  
Vol 39 (3) ◽  
pp. 657-667 ◽  
Author(s):  
Nan Pan ◽  
Xing Wu ◽  
Yu Guo
Keyword(s):  
Blind Deconvolution ◽  
Working Environment ◽  
Blind Separation ◽  
Deconvolution Algorithm ◽  
Morphological Filtering ◽  
Fuzzy C Means Clustering ◽  
The Impact ◽  
Periodic Components ◽  
Source Signals ◽  
Acoustic Diagnosis

In the progress of bearing fault acoustic testing, signals picked up by acoustic sensors are usually mixed with fault source signals and other noise signals due to the complexity of mechanical signals and various interference sources. In order to solve the above problems, an improved blind deconvolution algorithm is put forward. The proposed algorithm applies adaptive generalized morphological filtering to the observed signals to retain their characteristic details, and then utilizes an OMP algorithm based on the minimum kurtosis to restore the periodical signals in the mixed signals in order to reduce the impact of the periodic components on blind separation. Finally, the improved Kullback–Leibler (KL) distance algorithm is employed to calculate the distances between independent components, which is used as the clustering index, and then to perform fuzzy C-means clustering. The experiment results of bearing compound fault extraction in real working-environment demonstrate the accuracy and reliability of the proposed algorithm.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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