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.

An effective polarity correction method for microseismic migration-based location

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. KS115-KS125
Author(s):  
Jincheng Xu ◽  
Wei Zhang ◽  
Xiaofei Chen ◽  
Quanshi Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Real Data ◽  
Correction Method ◽  
Fitting Procedure ◽  
Microseismic Data ◽  
The Status ◽  
Event Location ◽  
Microseismic Events ◽  
Polarity Correction

Microseismic methods are important tools for monitoring the status and consequences of hydraulic fracturing. Because microseismic data recorded at the surface have a low signal-to-noise ratio, migration-based algorithms are widely used to determine the locations of microseismic events. However, there may be polarity changes in waveforms at different receivers due to the source mechanisms, which will cause the stacking images to not reach a maximum at the event location. One way for polarity correction is to perform the source mechanism and the source location inversions simultaneously, which, however, is computationally expensive and not good for real-time monitoring. We have developed an effective polarity correction method in the data domain for migration-based location methods called the polarity correction migration-based (PCM) method. This method uses an amplitude trend least-squares fitting procedure to determine the polarities along the receiver line with low additional computational cost. Then, the fitted waveform polarities are used to convert the signs of the amplitude values to stack them consistently. Due to curve fitting, this method is more suitable for microseismic data acquired with regular arrays than with scattered arrays. Numerical experiments of synthetic and real data sets demonstrate that the proposed PCM method can improve accuracy in the detection and location of microseismic events.

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.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. V79-V89 ◽  
Author(s):  
Wail A. Mousa ◽  
Abdullatif A. Al-Shuhail ◽  
Ayman Al-Lehyani
Keyword(s):  
Image Segmentation ◽  
Seismic Data ◽  
Convex Sets ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Color Image Segmentation ◽  
High Signal ◽  
Data Sets ◽  
First Arrival

We introduce a new method for first-arrival picking based on digital color-image segmentation of energy ratios of refracted seismic data. The method uses a new color-image segmentation scheme based on projection onto convex sets (POCS). The POCS requires a reference color for the first break and one iteration to segment the first-break amplitudes from other arrivals. We tested the segmentation method on synthetic seismic data sets with various amounts of additive Gaussian noise. The proposed method gives similar performance to a modified version of Coppens’ method for traces with high signal-to-noise ratio and medium-to-large offsets. Finally, we applied our method and used as well the modified first-arrival picking method based on Coppens’ method to pick the first arrivals on four real data sets, where both were compared to the first breaks that were picked manually and then interpolated. Based on an assessment error of a 20-ms window with respect to manual picks that are interpolated, we find that our method gives comparable performance to Coppens’ method, depending on the data difficulty of picking first arrivals. Therefore, we believe that our proposed method is a good new addition to the existing methods of first-arrival picking.

scholarly journals Fast and robust common-reflection-surface parameter estimation

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. O1-O13 ◽  
Author(s):  
Anders U. Waldeland ◽  
Hao Zhao ◽  
Jorge H. Faccipieri ◽  
Anne H. Schistad Solberg ◽  
Leiv-J. Gelius
Keyword(s):  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Real Data ◽  
Structure Tensor ◽  
Data Sets ◽  
Fast Method ◽  
Surface Parameter ◽  
Common Reflection Surface ◽  
The Common ◽  
The Cost

The common-reflection-surface (CRS) method offers a stack with higher signal-to-noise ratio at the cost of a time-consuming semblance search to obtain the stacking parameters. We have developed a fast method for extracting the CRS parameters using local slope and curvature. We estimate the slope and curvature with the gradient structure tensor and quadratic structure tensor on stacked data. This is done under the assumption that a stacking velocity is already available. Our method was compared with an existing slope-based method, in which the slope is extracted from prestack data. An experiment on synthetic data shows that our method has increased robustness against noise compared with the existing method. When applied to two real data sets, our method achieves accuracy comparable with the pragmatic and full semblance searches. Our method has the advantage of being approximately two and four orders of magnitude faster than the semblance searches.

Microseismic data denoising using a 3C group sparsity constrained time-frequency transform

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V21-V29 ◽  
Author(s):  
Ismael Vera Rodriguez ◽  
David Bonar ◽  
Mauricio Sacchi
Keyword(s):  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Data Sets ◽  
Group Sparsity ◽  
Time Frequency ◽  
Sparsity Pattern ◽  
Microseismic Data ◽  
Arbitrary Phase ◽  
Low Pass ◽  
Three Components

Noise contamination is an important problem in microseismic data processing, due to the low magnitude of the seismic events induced during fluid injection. In this study, a noncoherent noise attenuation technique based on a constrained time-frequency transform is presented. When applied to 1C data, the transform corresponds to a sparse representation of the microseismic signal in terms of a dictionary of complex Ricker wavelets. The use of complex wavelets possesses the advantage that signals with arbitrary phase can be represented with enhanced sparsity. A synthetic example illustrates the superior performance of the sparse constraint for denoising objectives when compared to the standard least-squares regularization. As the arrival time and frequency content of any wavefront are equivalent in the three components of a single receiver, the extension of the sparse transform to 3C data is accomplished when the three components are considered to share the same sparsity pattern in the time-frequency plane. Application of the 3C sparse transform to synthetic and real microseismic data sets demonstrate the advantages of this technique when the denoised results are compared against the original and low-pass filtered version of the noisy data. Furthermore, a comparison of hodograms between original, low-pass, and denoised traces shows that the denoising process preserves the phase and relative amplitude information present in the input data. The benefits of the 3C transform are highlighted particularly in cases where the wave arrivals are measured in the three components of a receiver but are only visible in two components due to the prevailing signal-to-noise ratio.

Convolutional neural networks for microseismic waveform classification and arrival picking

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA227-WA240 ◽  
Author(s):  
Guoyin Zhang ◽  
Chengyan Lin ◽  
Yangkang Chen
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Data Sets ◽  
Data Set ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Microseismic Data ◽  
Frequency Components ◽  
Specific Frequency

Microseismic data have a low signal-to-noise ratio (S/N). Existing waveform classification and arrival-picking methods are not effective enough for noisy microseismic data with low S/N. We have adopted a novel antinoise classifier for waveform classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep feedforward neural network (DNN), especially for microseismic data with low S/N. The CWT-CNN classifier has a shallow network architecture and small learning data set, and it can be trained quickly for different data sets. We have determined why CWT-CNN has better performance for noisy microseismic data. CWT can decompose the microseismic data into time-frequency spectra, where effective signals and interfering noise are easier to distinguish. With the help of CWT, CNN can focus on the specific frequency components to extract useful features and build a more effective classifier.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

scholarly journals Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Šárka Hudecová ◽  
Marie Hušková ◽  
Simos G. Meintanis
Keyword(s):  
Goodness Of Fit ◽  
Probability Generating Function ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Generalized Poisson ◽  
Goodness Of Fit Tests ◽  
Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

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