Signal-to-noise ratio enhancement for downhole microseismic data based on 3D shearlet transform

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. V207-V218
Author(s):  
Juan Li ◽  
Yuan Li ◽  
Shou Ji ◽  
Yue Li ◽  
Zhihong Qian
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Great Difficulty ◽  
Threshold Function ◽  
Signal To Noise ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
3D Matrix ◽  
Noise Ratio

Downhole microseismic data are characterized for their high frequency and small amplitude, which bring great difficulty for noise suppression. We present a random noise attenuation method for downhole microseismic data based on the 3D shearlet transform (3DST). In contrast to the 2D shearlet, 3DST takes into account the correlation among three components of downhole microseismic. With the help of correlation among the data, downhole microseismic data are reassembled into a new 3D matrix and then transformed to the shearlet domain. After the analysis of the coefficients’ energy and the high-order cumulant on each scale, an efficient threshold function is proposed. We apply a small threshold to the coefficients associated with the signal’s scales, and a large threshold is chosen for the scales of the noise. Experimental results indicate that the algorithm significantly improves the signal-to-noise ratio of the microseismic data and effectively preserves a valid signal.

Signal-to-noise ratio enhancement for 3C downhole microseismic data based on the 3D shearlet transform and improved back-propagation neural networks

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. V245-V254 ◽  
Author(s):  
Xintong Dong ◽  
Hong Jiang ◽  
Sheng Zheng ◽  
Yue Li ◽  
Baojun Yang
Keyword(s):  
Neural Networks ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Back Propagation ◽  
Bp Neural Networks ◽  
Signal To Noise ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Noise Ratio ◽  
Selection Of

As the seismic responses of unconventional hydraulic fracturing, downhole microseismic signals play an essential role in the exploitation of unconventional oil and gas reservoirs. In geologic structure interpretation and reservoir development, high-quality downhole microseismic data are necessary. However, the characteristics of downhole microseismic signals, such as weak energy and high frequency, bring great difficulty to signal-to-noise ratio enhancement. How to suppress the random noises in 3C downhole microseismic signals becomes problematic. To solve this problem, the 3D shearlet transform is introduced into downhole microseismic data processing. Different from the 2D shearlet transform, the correlation among the 3C of downhole microseismic signals is fully considered in the 3D shearlet transform, which enables the 3D shearlet transform to suppress random noise more effectively. In addition, for accurate selection of 3D shearlet coefficient, the back-propagation (BP) neural network is applied to the selection of coefficients. Unlike conventional threshold functions, BP neural networks can achieve optimal results by repeated training. At the same time, a new weight factor is proposed to improve the misconvergence of BP neural networks. Experimentally our method has been used to process synthetic and real 3C downhole microseismic signals, with results indicating that, compared with conventional methods, our new algorithm exhibits better performance in valid signal preservation and random noise suppression.

scholarly journals Application of a New Wavelet Threshold Method in Unconventional Oil and Gas Reservoir Seismic Data Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guxi Wang ◽  
Ling Chen ◽  
Si Guo ◽  
Yu Peng ◽  
Ke Guo
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Three Dimensional ◽  
Threshold Function ◽  
Signal To Noise ◽  
Gas Reservoir ◽  
Seismic Processing ◽  
Noise Ratio

Seismic data processing is an important aspect to improve the signal to noise ratio. The main work of this paper is to combine the characteristics of seismic data, using wavelet transform method, to eliminate and control such random noise, aiming to improve the signal to noise ratio and the technical methods used in large data systems, so that there can be better promotion and application. In recent years, prestack data denoising of all-digital three-dimensional seismic data is the key to data processing. Contrapose the characteristics of all-digital three-dimensional seismic data, and, on the basis of previous studies, a new threshold function is proposed. Comparing between conventional hard threshold and soft threshold, this function not only is easy to compute, but also has excellent mathematical properties and a clear physical meaning. The simulation results proved that this method can well remove the random noise. Using this threshold function in actual seismic processing of unconventional lithologic gas reservoir with low porosity, low permeability, low abundance, and strong heterogeneity, the results show that the denoising method can availably improve seismic processing effects and enhance the signal to noise ratio (SNR).

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

Method of Ultrasonic Signal De-Noising Based on Lifting Wavelet Improved Threshold

2014 ◽  
Vol 513-517 ◽  
pp. 3818-3821
Author(s):  
Zhou Yang Bi ◽  
Jian Hui Chen ◽  
Wen Jie Ju ◽  
Ming Wang ◽  
Ji Chen Li
Keyword(s):  
Signal To Noise Ratio ◽  
Ultrasonic Signal ◽  
Threshold Function ◽  
Time Signal ◽  
Mean Square ◽  
Signal To Noise ◽  
Ultrasonic Flaw ◽  
Lifting Wavelet ◽  
Noise Ratio ◽  
The Mathematical Model

The article established the mathematical model of ultrasonic flaw echo signals. First, the basic theory of wavelet transform is introduced, the principle of the wavelet threshold de-noising method is analyzed; Then on the basis of soft and hard threshold function, the paper proposes a method based on lifting wavelet de-noising. And from two aspects of signal-to-noise ratio (SNR) and mean square error (MSE) the de-noising performance is analysed. The results show that the method improved the shortcomings of soft and hard threshold de-noising method, and got a better de-noising performance and higher signal-to-noise ratio. So in real-time signal de-noising aspect the lifting wavelet has a very good application prospect.

Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry

2020 ◽  
Vol 26 (3) ◽  
pp. 204-212
Author(s):  
Anastasia Sarycheva ◽  
Alexey Adamov ◽  
Sergey S Poteshin ◽  
Sergey S Lagunov ◽  
Alexey A Sysoev
Keyword(s):  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Utilization Efficiency ◽  
Hadamard Transform ◽  
Signal Averaging ◽  
Signal To Noise ◽  
Pseudorandom Sequences ◽  
Noise Ratio

In Hadamard transform ion mobility spectrometry (HT IMS), the signal-to-noise ratio is always lower for non-modified pseudorandom sequences than for modified sequences. Since the use of non-modified modulating pseudorandom sequences is strategically preferable from a duty cycle standpoint, we investigated the change in the interference signal when transitioning from non-modified modulating sequences to sequences modified by the addition of 1,3,5 and 7 zeros. The interfering signal in HT IMS with modified pseudorandom sequences was shown to be mainly random noise for all the cases except for modifying by incorporation of 1 zero. For standard samples of tetraalkylammonium halides, modulation by non-modified pseudorandom sequences is beneficial in the case of small numbers of averaged spectra (below ∼40 averaged spectra compared to any modified pseudorandom sequences except for 1 zero modified and below ∼200 averaged spectra compared to signal averaging ion mobility spectrometry) and worsens the signal-to-noise ratio in the case of large numbers of averaged spectra. Contrarily, modulation by modified pseudorandom sequences is beneficial for any number of averaged spectra, except for very small ones (below 15 averaged spectra compared to modulation by non-modified sequences). Pseudorandom sequence modified with 1 zero incorporation is beneficial in the case of below ∼400 averaged spectra compared to any modified and non-modified pseudorandom sequences. The signal-to-noise ratio in conventional signal averaging mode ion mobility spectrometry is affected by random noise, whereas the HT IMS with non-modified pseudorandom sequences was demonstrated to be primarily affected by a systematic noise-like artefact signal. Because noise-like artefact signals were found to be reproducible, predicting models for interference signals could be generated to improve signal-to-noise ratio. This is significant because non-modified modulating sequences are limited by their poor signal-to-noise ratio. This improvement would increase the viability of non-modified modulating sequences which are preferred because of their higher sample utilization efficiency.

OCT Signal Processing Method Based on Sequential Kalman Filtering

2015 ◽  
Vol 1092-1093 ◽  
pp. 300-303 ◽  
Author(s):  
Yu Heng Yan ◽  
Yan Song Li
Keyword(s):  
Kalman Filter ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Current Transformer ◽  
Current Signal ◽  
Signal To Noise ◽  
Measured Current ◽  
Noise Characteristics ◽  
Noise Ratio

Optical current transformer (OCT) measured current signal which is mixed with strong random noise. The measured readings can’t accurately reflect the value of the measured current. Since the optical current transformer noise inside the band is basically where the measured current signal overlap,we can not use the traditional method to filter it out. This paper describes the measurement principle based on the Faraday effect of optical current transformer and signal to noise characteristics. Considering optical current transformer for low SNR characteristics, and embedded systems do not have the characteristics of a matrix library, we proposed using sequential Kalman filter to improve the real-time output signal to noise ratio. In the measured current for DC and AC conditions,we established an appropriate state space model Kalman filter.,and conduct simulation on matlab. Practice shows that the sequential Kalman filter algorithm can effectively improve the output signal to noise ratio and accuracy.

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