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.

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.

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.

Denoising seismic data using the nonlocal means algorithm

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. A5-A8 ◽  
Author(s):  
David Bonar ◽  
Mauricio Sacchi
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Seismic Energy ◽  
Real Data ◽  
Spatial Proximity ◽  
Data Sets ◽  
Noise Attenuation ◽  
Nonlocal Means ◽  
Reflection Seismic ◽  
Generally True

The nonlocal means algorithm is a noise attenuation filter that was originally developed for the purposes of image denoising. This algorithm denoises each sample or pixel within an image by utilizing other similar samples or pixels regardless of their spatial proximity, making the process nonlocal. Such a technique places no assumptions on the data except that structures within the data contain a degree of redundancy. Because this is generally true for reflection seismic data, we propose to adopt the nonlocal means algorithm to attenuate random noise in seismic data. Tests with synthetic and real data sets demonstrate that the nonlocal means algorithm does not smear seismic energy across sharp discontinuities or curved events when compared to seismic denoising methods such as f-x deconvolution.

scholarly journals Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1089
Author(s):  
Huailai Zhou ◽  
Yangqin Guo ◽  
Ke Guo
Keyword(s):  
Neural Network ◽  
Noise Suppression ◽  
Anthropogenic Impacts ◽  
Random Noise ◽  
Real Data ◽  
Seismic Signal ◽  
Input Noise ◽  
Work Area ◽  
Convolutional Autoencoder ◽  
Noise Data

Random noise is unavoidable in seismic data acquisition due to anthropogenic impacts or environmental influences. Therefore, random noise suppression is a fundamental procedure in seismic signal processing. Herein, a deep denoising convolutional autoencoder network based on self-supervised learning was developed herein to attenuate seismic random noise. Unlike conventional methods, our approach did not use synthetic clean data or denoising results as a training label to build the training and test sets. We directly used patches of raw noise data to establish the training set. Subsequently, we designed a robust deep convolutional neural network (CNN), which only depended on the input noise dataset to learn hidden features. The mean square error was then evaluated to establish the cost function. Additionally, tied weights were used to reduce the risk of over-fitting and improve the training speed to tune the network parameters. Finally, we denoised the target work area signals using the trained CNN network. The final denoising result was obtained after patch recombination and inverse operation. Results based on synthetic and real data indicated that the proposed method performs better than other novel denoising methods without loss of signal quality loss.

scholarly journals Weighted Bagging in Decision Trees: Data Mining

2020 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Yousef Elgimati
Keyword(s):  
Decision Trees ◽  
Learning Algorithm ◽  
Majority Vote ◽  
Random Noise ◽  
Real Data ◽  
Data Sets ◽  
Bootstrap Sample ◽  
Training Set ◽  
Multiple Classifier ◽  
Better Than

The main focus of this paper is on the use of resampling techniques to construct predictive models from data and the goal is to identify the best possible model which can produce better predications. Bagging or Bootstrap aggregating is a general method for improving the performance of given learning algorithm by using a majority vote to combine multiple classifier outputs derived from a single classifier on a bootstrap resample version of a training set. A bootstrap sample is generated by a random sample with replacement from the original training set. Inspired by the idea of bagging, we present an improved method based on a distance function in decision trees, called modified bagging (or weighted Bagging) in this study. The experimental results show that modified bagging is superior to the usual majority vote. These results are confirmed by both real data and artificial data sets with random noise. The Modified bagged classifier performs significantly better than usual bagging on various tree levels for all sample sizes. An interesting observation is that the weighted bagging performs somewhat better than usual bagging with sumps.

scholarly journals Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

2021 ◽  
Author(s):  
Rigobert Tibi ◽  
Patrick Hammond ◽  
Ronald Brogan ◽  
Christopher J. Young ◽  
Keith Koper
Keyword(s):  
Input Signal ◽  
Noise Suppression ◽  
Fundamental Problem ◽  
Real Data ◽  
Denoising Method ◽  
Similar Frequency ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Band Pass ◽  
Music Information

ABSTRACT Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ∼5  dB, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ∼0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ∼2–5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ∼15  dB.

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.

Minimum semblance weighted stacking with polarity correction for surface microseismic data processing

2019 ◽  
Vol 38 (8) ◽  
pp. 630-636 ◽  
Author(s):  
Jincheng Xu ◽  
Wei Zhang ◽  
Xaofei Chen ◽  
Quanshi Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Real Data ◽  
Data Sets ◽  
Source Imaging ◽  
Microseismic Data ◽  
True Source ◽  
Source Mechanisms ◽  
Polarity Correction ◽  
Microseismic Location ◽  
Location Methods

Diffraction-stack-based algorithms are the most popular microseismic location methods for surface microseismic data. They can accommodate microseismic data with low signal-to-noise ratio by stacking a large number of traces. However, changes in waveform polarity across the receiver line due to source mechanisms may prevent stacking methods from locating the true source. Imaging functions based on simple stacks have low resolution, producing large uncertainty in the final location result. To solve these issues, we introduce a minimum semblance weighted stacking method with polarity correction, which uses an amplitude trend least-squares fitting algorithm to correct the polarity across the receiver line. We adapt the semblance weighted stacking for better coherency measure to improve the imaging resolution. Moreover, the minimum semblance is used to further improve the resolution of location results. Application to both synthetic and real data sets demonstrates good performance of our proposed location method.

scholarly journals Resolution Enhancement of GPR Data Using Bayesian Filtering Optimized by Machine Learning Algorithms

2020 ◽  
Author(s):  
Alireza HAJIAN ◽  
Rohollah Kimiaefar
Keyword(s):  
Fuzzy Inference ◽  
Random Noise ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Real Data ◽  
Machine Learning Algorithms ◽  
Environmental Study ◽  
Data Sets ◽  
Bayesian Filtering ◽  
Inference System

Abstract Blurring coherence events is the result of applying many spatial and temporal filtering algorithmswhen they are applied in order to suppress background random noise. Bayesian Filtering (BF) also suffers from mentioned problem. This paper develops a method for optimizing BF by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy C-Mean (FCM)clustering. First the structure of the GPR image is extracted using FCM. The structure and output of the BF for a random part of the data are used to produce output values for training ANFIS and after that, by generalizing the trained network to all data, filtered data would be achieved. The proposed method is applied on synthetic data-sets as well as tworeal 2-D GPR images gathered in an environmental study project. Performance of the method is evaluated by comparing the results of the proposed method to the output of BF. In synthetic data, the SNR value improved 63 percent more than of BF’s outputandthe visual comparison of the results are suggesting better performance in noise cancellation and resolution enhancement, both in synthetic and real data-sets.

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