Least-squares decomposition with time–space constraint for denoising microseismic data

2020 ◽  
Vol 222 (3) ◽  
pp. 1864-1880
Author(s):  
Yangkang Chen ◽  
Wei Chen ◽  
Yufeng Wang ◽  
Min Bai
Keyword(s):  
Least Squares ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Microseismic Data ◽  
Smoothness Constraint ◽  
Time Space ◽  
Wide Range ◽  
The Stability ◽  
And Inversion

SUMMARY Microseismic data are usually of low signal-to-noise ratio (SNR), which makes it difficult to utilize the microseismic waveforms for imaging and inversion. We develop a useful denoising algorithm based on a non-stationary least-squares decomposition model to enhance the quality of microseismic signals. The microseismic signals are assumed to be represented by a superposition of several smoothly variable components. We construct a least-squares inverse problem to solve for the the smooth components. We constrain the least-squares inversion via both time and space constraints. The temporal smoothness constraint is applied to ensure the stability when calculating the non-stationary autoregression coefficients. The space-smoothness constraint is applied to extract the spatial correlation among multichannel microseismic traces. The new algorithm is validated via several synthetic and real microseismic data and are proved to be effective. Comparison with the state-of-the-art algorithms demonstrates that the proposed method is more powerful in suppressing random noise of a wide range of levels than its competing methods.

Research of Wavelet Packet Analysis in the De-Noising of Ultrasonic Echo Signal

2011 ◽  
Vol 403-408 ◽  
pp. 1817-1822
Author(s):  
Xi Feng Zhou ◽  
Xiao Wu ◽  
Qian Gang Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Random Noise ◽  
Flaw Detection ◽  
Low Frequency ◽  
Local Analysis ◽  
Echo Signal ◽  
Wavelet Packet Analysis ◽  
Ultrasonic Flaw

The quality of ultrasonic flaw echo signal is the foundation of achieving qualitative and quantitative analysis in the in ultrasonic flaw detection. In practice, the flaw echo signals are often contaminated or even annihilation by random noise. According to the characteristics of ultrasonic flaw echo signal, wavelet packet has more accurate local analysis ability in low frequency and high frequency part. This paper discusses de-noising in ultrasonic signals based on wavelet packet analysis, and proposes an improved threshold approach for de-noising. The results show that: It remarkably raises the signal-to-noise ratio of ultrasonic flaw echo signal and improves the quality of signal with improved wavelet packet threshold.

scholarly journals New Approaches to Processing Ground-based SAR (GBSAR) Data for Deformation Monitoring

2018 ◽  
Vol 10 (12) ◽  
pp. 1936 ◽  
Author(s):  
Sichun Long ◽  
Aixia Tong ◽  
Ying Yuan ◽  
Zhenhong Li ◽  
Wenhao Wu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Atmospheric Correction ◽  
Deformation Monitoring ◽  
Atmospheric Effects ◽  
Threshold Optimization ◽  
Before And After ◽  
Multiple Threshold ◽  
The Stability ◽  
Average Coherence

In this paper, aiming at the limitation of persistence scatterers (PS) points selection, a new method for selecting PS points has been introduced based on the average coherence coefficient, amplitude dispersion index, estimated signal-to-noise ratio and displacement standard deviation of multiple threshold optimization. The stability and quality of this method are better than that of a single model. In addition, an atmospheric correction model has also been proposed to estimate the atmospheric effects on Ground-based synthetic aperture radar (GBSAR) observations. After comparing the monitoring results before and after correction, we clearly found that the results are in good agreement with the actual observations after applying the proposed atmospheric correction approach.

3-D coherency filtering

Geophysics ◽  
1997 ◽  
Vol 62 (4) ◽  
pp. 1310-1314 ◽  
Author(s):  
Qing Li ◽  
Kris Vasudevan ◽  
Frederick A. Cook
Keyword(s):  
Seismic Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Three Dimensions ◽  
Coherent Noise ◽  
Coherent Signal ◽  
Frequency Space ◽  
Time Space ◽  
Incoherent Noise

Coherency filtering is a tool used commonly in 2-D seismic processing to isolate desired events from noisy data. It assumes that phase‐coherent signal can be separated from background incoherent noise on the basis of coherency estimates, and coherent noise from coherent signal on the basis of different dips. It is achieved by searching for the maximum coherence direction for each data point of a seismic event and enhancing the event along this direction through stacking; it suppresses the incoherent events along other directions. Foundations for a 2-D coherency filtering algorithm were laid out by several researchers (Neidell and Taner, 1971; McMechan, 1983; Leven and Roy‐Chowdhury, 1984; Kong et al., 1985; Milkereit and Spencer, 1989). Milkereit and Spencer (1989) have applied 2-D coherency filtering successfully to 2-D deep crustal seismic data for the improvement of visualization and interpretation. Work on random noise attenuation using frequency‐space or time‐space prediction filters both in two or three dimensions to increase the signal‐to‐noise ratio of the data can be found in geophysical literature (Canales, 1984; Hornbostel, 1991; Abma and Claerbout, 1995).

Low-Power On-The-Fly Reconfigurable Iterative MIMO Detection and LDPC Decoding Design

2014 ◽  
Vol 496-500 ◽  
pp. 1825-1829 ◽  
Author(s):  
Ehsan Rohani ◽  
Jing Wei Xu ◽  
Gwan Choi ◽  
Mi Lu
Keyword(s):  
Low Power ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Wireless Systems ◽  
Operating Conditions ◽  
Mimo Detection ◽  
Ldpc Decoding ◽  
Wide Range ◽  
Multiple Input

Manufacturing and operation of wireless systems require a practical solution for achieving low-power and high-performance when using advance communication apparatus such as that using multiple-input and multiple-output (MIMO). Often algorithm solutions achieve very high performance but over only in a narrow range of operating parameters. This paper presents a hardware design of MIMO detection that allows real-time switching between various algorithms and detection effort to achieve high performance over the wide-range of signal to noise ratio (SNR) found in realistic operating conditions. We illustrate a design with over 80% reduction in detection power that satisfies the required quality of service (QoS) in SNRs (Eb/No) as low as 8.7 dB.

Stacking seismic data using local correlation

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. V43-V48 ◽  
Author(s):  
Guochang Liu ◽  
Sergey Fomel ◽  
Long Jin ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Image Point ◽  
Local Correlation ◽  
Seismic Profiles ◽  
Signal To Noise ◽  
Prestack Migration

Stacking plays an important role in improving signal-to-noise ratio and imaging quality of seismic data. However, for low-fold-coverage seismic profiles, the result of conventional stacking is not always satisfactory. To address this problem, we have developed a method of stacking in which we use local correlation as a weight for stacking common-midpoint gathers after NMO processing or common-image-point gathers after prestack migration. Application of the method to synthetic and field data showed that stacking using local correlation can be more effective in suppressing random noise and artifacts than other stacking methods.

scholarly journals Image Simulation for Mingantu Ultrawide Spectral Radioheliograph in the Decimetre Wave Range

2015 ◽  
Vol 32 ◽  
Author(s):  
Jing Du ◽  
Yihua Yan ◽  
Wei Wang ◽  
Donghao Liu
Keyword(s):  
Inner Mongolia ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Frequency Resolution ◽  
The Sun ◽  
Image Simulation ◽  
Time Space ◽  
Wide Range ◽  
Wave Range ◽  
Imaging Performance

AbstractThe MUSER is a solar-dedicated radio interferometric array, which will observe the Sun over a wide range of radio frequencies (0.4–15 GHz), and make high time, space and frequency resolution images of the Sun simultaneously. MUSER is located in Mingantu Station in Inner Mongolia of China, which is about 400 kilometres away from Beijing. MUSER consists of two arrays: MUSER-I and MUSER-II. MUSER-I contains 40 antennas with 4.5-m aperture operating at 400 MHz to 2 GHz. MUSER-II contains 60 antennas with 2-m aperture operating at 2 to 15 GHz. Currently, MUSER has already been established and entered into the stage of test observation. This work is focus on the imaging performance of MUSER-I. This paper introduces MUSER-I briefly, presents the analysis of the array configurations, and evaluates the image quality mainly using the dynamic range, fidelity index, and the peak signal-to-noise ratio, also make some actual solar model simulations with CASA, the results will be shown below.

scholarly journals Providing Invariance to Disturbing Effects in Rail Lines

2019 ◽  
pp. 152-168 ◽  
Author(s):  
Evgeniy M. Tarasov ◽  
Dmitry V. Zheleznov ◽  
Nicolay N. Vasin ◽  
Anna E. Tarasova
Keyword(s):  
Dynamic Range ◽  
Input Resistance ◽  
Indirect Measurement ◽  
Open Circuit ◽  
Time Interval ◽  
Informative Signal ◽  
Wide Range ◽  
Rail Line ◽  
The Stability

Introduction. The time interval systems for controlling train movement operated under the influence of significant industrial disturbances, interference from the electric current of traction rolling stocks, and significant climate changes that result in fluctuations of parameters of circuit elements. These factors lead to the appearance of internal disturbances. The fluctuations in a wide range of the conductivity of rail lines insulation are the main external disturbances leading to considerable changes of the informative parameter, the voltage at the output end of the rail line. At present, there are many methods for suppressing disturbances, which allow correcting fluctuations in the informative signal without deteriorating the quality of classification. The article deals with the problem of providing insensitivity of the output informative signal to the influence of disturbance by principles of coordinate compensation with a correcting link. Materials and Methods. To solve the problem, various methodologies of compensation for disturbances are considered in the paper; the method of coordinate compensation for disturbances at the input of a quadripole of rail lines is adopted as the main one. The equation of the transfer function of the correcting link is determined, assuming an indirect measurement of the input resistance of the rail line, which is a function of the conductivity of the insulation. Results. The article presents the results of the research of the invariant capabilities of the disturbance compensation principle. It is shown that disturbances compensation with a corrective link included at the input of a quadripole allows one to significantly reduce the dynamic range of the output informative signal change in each of the classes, i.e. classes have become more compact, and the quality of classification has become 5 times higher than in the absence of compensation of disturbances. Discussion and Conclusion. The results confirm the effectiveness of the proposed method for the coordinate compensation of disturbances in rail lines with an open circuit in the absence of the possibility for organizing feedback, a variable circuit in each of the classes of states, and the impossibility of creating a physical additional channel for the transmission of the disturbance. Using the proposed method in the construction of modern classifiers will significantly improve the stability of the functioning of train control systems; eliminate errors of the first kind, leading to unproductive idle train, and errors of the second kind, leading to accidents and crashes.

Self-training and learning the waveform features of microseismic data using an adaptive dictionary

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS51-KS61 ◽  
Author(s):  
Hang Wang ◽  
Quan Zhang ◽  
Guoyin Zhang ◽  
Jinwei Fang ◽  
Yangkang Chen
Keyword(s):  
Dictionary Learning ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Microseismic Monitoring ◽  
Training Data ◽  
Learning Framework ◽  
Microseismic Data ◽  
Useful Signal ◽  
Fracturing Process

Microseismic monitoring is an indispensable technique in characterizing the physical processes that are caused by extraction or injection of fluids during the hydraulic fracturing process. Microseismic data, however, are often contaminated with strong random noise and have a low signal-to-noise ratio (S/N). The low S/N in most microseismic data severely affects the accuracy and reliability of the source localization and source-mechanism inversion results. We have developed a new denoising framework to enhance the quality of microseismic data. We use the method of adaptive sparse dictionaries to learn the waveform features of the microseismic data by iteratively updating the dictionary atoms and sparse coefficients in an unsupervised way. Unlike most existing dictionary learning applications in the seismic community, we learn the features from 1D microseismic data, thereby to learn 1D features of the waveforms. We develop a sparse dictionary learning framework and then prepare the training patches and implement the algorithm to obtain favorable denoising performance. We use extensive numerical examples and real microseismic data examples to demonstrate the validity of our method. Results show that the features of microseismic waveforms can be learned to distinguish signal patches and noise patches even from a single channel of microseismic data. However, more training data can make the learned features smoother and better at representing useful signal components.

scholarly journals Expression of Concern: Least-squares decomposition with time–space constraint for denoising microseismic data

2020 ◽  
Vol 221 (3) ◽  
pp. 2055-2055
Author(s):  
Yangkang Chen ◽  
Wei Chen ◽  
Yufeng Wang ◽  
Min Bai
Keyword(s):  
Least Squares ◽  
Space Constraint ◽  
Microseismic Data ◽  
Time Space

scholarly journals Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. KS155-KS172
Author(s):  
Jie Shao ◽  
Yibo Wang ◽  
Yi Yao ◽  
Shaojiang Wu ◽  
Qingfeng Xue ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Time Consistency ◽  
Denoising Method ◽  
Microseismic Data ◽  
Joint Sparse Representation ◽  
Consistency Constraint

Microseismic data usually have a low signal-to-noise ratio, necessitating the application of an effective denoising method. Most conventional denoising methods treat each component of multicomponent data separately, e.g., denoising methods with sparse representation. However, microseismic data are often acquired with a 3C receiver, especially in borehole monitoring cases. Independent denoising ignores the relative amplitudes and vector relationships between different components. We have developed a new simultaneous denoising method for 3C microseismic data based on joint sparse representation. The three components are represented by different dictionary atoms; the dictionary can be fixed or adaptive depending on the dictionary learning method that is used. Our method adds an extra time consistency constraint with simultaneous transformation of 3C data. The joint sparse optimization problem is solved using the extended orthogonal matching pursuit. Synthetic microseismic data with a double-couple source mechanism and two field downhole microseismic data were used for testing. Independent denoising of 1C data with the fixed dictionary method and simultaneous denoising of 3C data with the fixed dictionary and dictionary learning (3C-DL) methods were compared. The results indicate that among the three methods, the 3C-DL method is the most effective in suppressing random noise, preserving weak signals, and restoring polarization information; this is achieved by combining the time consistency constraint and dictionary learning.

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