A hybrid correlation denoising method based on complex resistivity and application on spread spectrum induced polarization data

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
Siming He ◽  
Jian Guan ◽  
Yi Wang ◽  
Xiu Ji ◽  
Hui Wang
Keyword(s):  
Hybrid Method ◽  
Spread Spectrum ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Induced Polarization ◽  
Data Simulation ◽  
Denoising Method ◽  
Complex Resistivity ◽  
Geophysical Surveys

In electrical exploration techniques, an effective suppression method for Gaussian and impulsive random noise in spread spectrum induced polarization (SSIP) continues to be challenging for conventional denoising methods. Remnant noise influences the complex resistivity spectrum and damages the subsequent interpretation of geophysical surveys. We present a hybrid method based on a correlation function and complex resistivity, which introduces the correlation analyses between the transmitting source, the measured potential, and the injected current signal. According to the analyses, reliable results for complex resistivity spectra can be calculated, which can be further used for noise suppression. We apply the hybrid method to both numerical and field experiments to process measured SSIP data. Simulation tests show that the hybrid method not only suppresses the two types of noise but also improves the relative error of the complex resistivity spectrum. Field data processing shows that the hybrid method can minimize the standard deviation of the data and possess a greater ability to distinguish adjacent objects, which can improve the reliability of the data in subsequent processing and interpretation.

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.

scholarly journals Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

2019 ◽  
Vol 11 (15) ◽  
pp. 1829 ◽  
Author(s):  
Yao ◽  
Zhang ◽  
Yu ◽  
Zhao ◽  
Sun
Keyword(s):  
Magnetic Resonance ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Kernel Regression ◽  
Sparse Reconstruction ◽  
Regression Estimation ◽  
Magnetic Resonance Sounding ◽  
Kernel Regression Estimation ◽  
Intensive Sampling

The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line.

Correlation analysis for spread-spectrum induced-polarization signal processing in electromagnetically noisy environments

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. E243-E256 ◽  
Author(s):  
Weiqiang Liu ◽  
Rujun Chen ◽  
Hongzhu Cai ◽  
Weibin Luo ◽  
André Revil
Keyword(s):  
Signal Processing ◽  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Spread Spectrum ◽  
Threshold Value ◽  
Induced Polarization ◽  
Gansu Province ◽  
Data Set ◽  
Complex Resistivity ◽  
Primary Current

In induced-polarization (IP) surveys, the raw data are usually distorted significantly by the presence of electromagnetic (EM) interferences, including cultural noise. Several methods have been proposed to improve the signal-to-noise ratio of these data. However, signal processing in an electromagnetically noisy environment is still a challenging problem. We have determined a new and simple technique based on the analysis of the correlation between the measured potential and the injected primary current signals. This processing is applied to the data acquired using a new frequency-domain IP method called the spread-spectrum induced-polarization (SSIP) approach. In this approach, we use a pseudorandom m-sequence (also called the maximum length sequence) for the injected primary current. One of the advantages of this sequence is to be essentially spectrally flat in a given frequency range. Therefore, complex resistivity can be determined simultaneously at various frequencies. A new SSIP data set is acquired in the vicinity of Baiyin mine, Gansu Province, China. The correlation between potential difference and transmitting current signals for each period can be used to assess data quality. Only when the correlation coefficient between the two signals is greater than 0.5 can the SSIP data be used for subsequent processing and tomography. We determine what threshold value should be used for the correlation coefficient to extract high-quality apparent complex resistivity data and eliminate EM-contaminated data. We then compare the pseudosections with and without using the correlation analysis. When the correlation analysis is used, the noisy data are filtered out, and the target anomaly obtained through tomography is clearly enhanced. The inversion results of the apparent complex resistivity (amplitude and phase) for the survey area are consistent with some independent geologic and drilling information regarding the position of the ore body demonstrating the effectiveness of the approach.

scholarly journals Application of an improved particle filter for random seismic noise suppression

2021 ◽  
Vol 18 (6) ◽  
pp. 943-953
Author(s):  
Jingquan Zhang ◽  
Dian Wang ◽  
Peng Li ◽  
Shiyu Liu ◽  
Han Yu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Seismic Data ◽  
Particle Filtering ◽  
Seismic Noise ◽  
Firefly Algorithm ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Seismic Signals ◽  
And Performance

Abstract Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

scholarly journals GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1269
Author(s):  
Jiabin Luo ◽  
Wentai Lei ◽  
Feifei Hou ◽  
Chenghao Wang ◽  
Qiang Ren ◽  
...  
Keyword(s):  
Image Denoising ◽  
Data Augmentation ◽  
Noise Suppression ◽  
Random Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Convolutional Autoencoder

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

On: “Recent Advances and Applications in Complex Resistivity Measurements,” by K. L. Zonge and J. C. Wynn (GEOPHYSICS, Oct. 1975, p. 851–864)

Geophysics ◽  
1977 ◽  
Vol 42 (1) ◽  
pp. 120-121 ◽  
Author(s):  
P. H. Nelson ◽  
G. D. Van Voorhis
Keyword(s):  
Spectral Data ◽  
Induced Polarization ◽  
Complex Resistivity ◽  
Resistivity Measurements ◽  
Recent Advances ◽  
Measuring Techniques

In presenting a variety of induced polarization spectral data, Zonge and Wynn refer to a paper published earlier by us (Van Voorhis et al., 1973) which deals with the same topic. We feel Zonge and Wynn have misrepresented our measuring techniques, data, and conclusions in their references to our paper. Our principal objections center on three statements by the authors.

MOS imaging with random noise suppression

1984 ◽  
Author(s):  
S. Ohba ◽  
M. Nakai ◽  
H. Ando ◽  
T. Ozaki ◽  
N. Ozawa ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Random Noise

scholarly journals An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

2021 ◽  
Vol 28 (2) ◽  
pp. 247-256
Author(s):  
Siming He ◽  
Jian Guan ◽  
Xiu Ji ◽  
Hang Xu ◽  
Yi Wang
Keyword(s):  
Data Processing ◽  
Spread Spectrum ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Induced Polarization ◽  
High Resistivity ◽  
Observation System ◽  
Pseudorandom Sequence ◽  
Identification Algorithm ◽  
The Time Domain

Abstract. In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The time-domain spectral induced polarization based on pseudorandom sequence (TSIP) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the frequency-domain IP (FDIP) method is used for extracting the frequency response of the observation system. Experiments on both synthetic and real SSIP data show that the ECI algorithm will not only suppress the background noise but also better preserve the valid information of the raw SSIP data to display the actual location and shape of adjacent high-resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging.

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