scholarly journals A Combined Denoising Method for Microseismic Signals from Coal Seam Hydraulic Fracturing: Multithreshold Wavelet Packet Transform and Improved Hilbert-Huang Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhengxing Yu ◽  
Jinglin Wen ◽  
Quanjie Zhu ◽  
Haitao Ma ◽  
Yu Feng
Keyword(s):  
Coal Seam ◽  
Hydraulic Fracturing ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Complex Nature ◽  
Signal To Noise ◽  
Denoising Method ◽  
Hilbert Huang Transform ◽  
Noise Ratio

Coal seam hydraulic fracturing (CSHF) has recently been applied to mitigate frequent regional rockburst risk in deep mines before mining practice, as an effective substitute for conventional labor-intensive and time-consuming rockburst prevention measures. Due to the complex nature of CSHF microseismic signals—e.g., nonstationary, transient, and low signal-to-noise ratio—conventional denoising methods tend to yield undesirable results that may preclude reliable evaluation of hydraulic fracturing performance using microseismic data. We propose an advanced denoising method MWPT-IHHT to achieve twice denoising in a fine and adaptive manner. This method combines a multithreshold wavelet packet transform (MWPT) and an improved Hilbert-Huang transform (IHHT), with each being improved compared to their conventional counterparts. A quantitative comparison using synthetic signals suggests the outperformance of the proposed method over the commonly used denoising methods in suppressing noises in terms of signal-to-noise ratio, signal similarity, and energy percentage. The desirable denoising results of two typical real CSHF signals in a CSHF test at Huafeng Coal Mine further demonstrate the applicability and effectiveness of the proposed MWPT-IHHT method.

Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

2015 ◽  
Author(s):  
Jinjiang Wang ◽  
Robert X. Gao ◽  
Xinyao Tang ◽  
Zhaoyan Fan ◽  
Peng Wang
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Experimental Studies ◽  
Wavelet Packet Transform ◽  
Signal To Noise ◽  
Metallic Structures ◽  
Time Frequency ◽  
Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

Research on LIFT Technology for 3D Seismic Denoising in Coalfield

2014 ◽  
Vol 556-562 ◽  
pp. 6328-6331
Author(s):  
Su Zhen Shi ◽  
Yi Chen Zhao ◽  
Li Biao Yang ◽  
Yao Tang ◽  
Juan Li
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
3D Seismic ◽  
Signal To Noise ◽  
Denoising Method ◽  
Seismic Processing ◽  
Work Area ◽  
Noise Data ◽  
Noise Ratio ◽  
Effective Signal

The LIFT technology has applied in process of denoising to ensure the imaging precision of minor faults and structure in 3D coalfield seismic processing. The paper focused on the denoising process in two study areas where the LIFT technology is used. The separation of signal and noise is done firstly. Then denoising would be done in the noise data. The Data of weak effective signal that is from the noise data could be blended with the original effective signal to reconstruct the denoising data, so the result which has high signal-to-noise ratio and preserved amplitude is acquired. Thus the fact shows that LIFT is an effective denoising method for 3D seismic in coalfield and could be used widely in other work area.

Research on Method for Obtaining Time Difference of Localization of Transformer PD Source

2013 ◽  
Vol 291-294 ◽  
pp. 2222-2227
Author(s):  
Hong Ling Xie ◽  
Bo Yi Shen ◽  
Fei Long Wang ◽  
Yan Qing Li
Keyword(s):  
Signal To Noise Ratio ◽  
Ultrasonic Signal ◽  
Time Difference ◽  
Interference Signal ◽  
Signal To Noise ◽  
Hilbert Huang Transform ◽  
Numerical Result ◽  
Fast Independent Component Analysis ◽  
Noise Ratio ◽  
Precise Moment

Ultrasonic positioning accuracy of transformer partial discharge is not high. The main reason is that it is difficult to obtain accurate time difference of the ultrasonic signals reaching two different sensors, especially when the signal-to-noise ratio is not high enough. To solve this problem, Hilbert-Huang transform (HHT) is applied in signal processing to extract the precise moment, and in this way the precise time difference is easily obtained. The signal-to-noise ratio (SNR) of received signal is low, and even worse PD signal is drowned in the strong interference signal. For this, the method named fast independent component analysis (FastICA) is applied in ultrasonic signal denoising before obtaining the time difference. FastICA is not effective in separating the ultrasonic signal from the separated components and in order to solve the problem, envelope identification method is proposed in this paper. In the method, the upper envelope of the signal waveform is sampled and the average of the sampling points is calculated to extract the ultrasonic signal. The numerical result confirmed the practicality of all the mentioned methods.

A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation

2018 ◽  
Vol 18 (04) ◽  
pp. 1850023 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Homayun Motameni
Keyword(s):  
Genetic Algorithm ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Bilateral Filter ◽  
Fuzzy Evaluation ◽  
Signal To Noise ◽  
Denoising Method ◽  
Final Model ◽  
Noise Ratio

In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.

Research on Application of Wavelet Denoising Method Based on Signal to Noise Ratio in the Bench Test

2013 ◽  
Vol 457-458 ◽  
pp. 1156-1162 ◽  
Author(s):  
Jian Jun Zhong ◽  
Sheng Nan Fang ◽  
Chang Ying Linghu
Keyword(s):  
Signal To Noise Ratio ◽  
Basis Functions ◽  
Bench Test ◽  
Signal Denoising ◽  
Wavelet Basis ◽  
Signal To Noise ◽  
Denoising Method ◽  
Acceleration Signal ◽  
Noise Ratio ◽  
Filtering Effect

During the tests of the vehicle automatic transmission bench, the acceleration signal is needed to be denoised. As a means of denoising, wavelet threshold denoising method has small amount of calculation and better filtering effect. However, adopting different wavelet basis functions as well as different threshold rules might have a direct effect on the signal denoising. In this paper, we firstly construct the simulated noisy signal approximated to the observed signal, and then do the signal denoising experiment of parameter matching. Secondly, seven Symlets wavelet basis functions and four classical wavelet threshold rules are selected and tested one by one. Signal to noise ratio (SNR) and root mean square error (RMSE) of the denoised signal, the evaluation indicators, are calculated and carried out in accordance with the merits of denoising effect. Thus the optimal combination of the fixed threshold rule and sym8 wavelet basis function is obtained. Finally, this combination is used in the bench test to denoise the angular acceleration signal, and good filtering effect is achieved.

Application of Wavelet Method on Preprocessing of Voltage Signals

2010 ◽  
Vol 40-41 ◽  
pp. 272-276
Author(s):  
Li Di Wang ◽  
Nan Zhu ◽  
Jin Kai Li
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Electrical Parameter ◽  
Wavelet Denoising ◽  
Signal To Noise ◽  
Denoising Method ◽  
Wavelet Method ◽  
Squared Error ◽  
Noise Ratio

Wavelet denoising method is applied in the measurement voltage signals in this paper. Noise reduction is important for signal preprocessing in order to achieve many objects such as the improvement of accuracy of modal analysis and electrical parameter identification, the effective extraction of features and auto-matic classification of different kinds of signals. The voltage signals measured from one 35Kv bus are used for the preprocessing research. The denoising effect is evaluated by three parameters, i.e. signal to noise ratio, mean squared error, and capture ability of step points. Compared with the traditional methods including mean filtering and medial filtering, wavelet method is superior in signal to noise ratio and mean squared error.

Comparison between Mean Filter and Median Filter Algorithm in Image Denoising Field

2014 ◽  
Vol 644-650 ◽  
pp. 4112-4116 ◽  
Author(s):  
Xiao Xin Sun ◽  
Wei Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Signal To Noise ◽  
Denoising Method ◽  
Mean Filter ◽  
Noise Ratio

An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.

Catenary image denoising method using lifting wavelet-based contourlet transform with cycle shift-invariance

2019 ◽  
Vol 17 (01) ◽  
pp. 1850060 ◽  
Author(s):  
Changdong Wu ◽  
Hua Jiang
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Contourlet Transform ◽  
Signal To Noise ◽  
Denoising Method ◽  
Shift Invariance ◽  
Lifting Wavelet ◽  
Noise Ratio

In the catenary status detection system based on the image processing, quality of the captured catenary image is critical. In order to obtain a high quality image for further analysis, this paper proposes a new catenary image denoising method based on lifting wavelet-based contourlet transform with cycle shift-invariance (LWBCTCS). In this method, the lifting wavelet is first constructed based on wavelet transform (WT). Then, to decrease the redundancy of contourlet transform (CT), the lifting wavelet-based contourlet transform (LWBCT) is built by using the lifting wavelet to replace the Laplacian pyramid (LP) transform of CT. Finally, the LWBCT with the cycle shift-invariance (LWBCTCS) algorithm is combined to reduce the pseudo-Gibbs phenomena of LWBCT. The proposed method not only has the virtues of multi-scale and multi-direction, but also reduces the visual artifacts in the denoised image. The results of comparative experiments with captured catenary image show that the proposed method can achieve satisfactory denoising performance, in particular, for catenary image with abundant texture and detail outline information. It not only eliminates noise but also preserves the textures and details simultaneously. Besides, comprehensive consideration of the denoising performance shows that the proposed algorithm in terms of the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean squared error (MSE) is stable than those conventional denoising algorithms, including WT, CT, curvelet transform (CV) and BLS-GSM methods. The visual quality as well as quantitative metrics is superior than those conventional denoising methods.

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