The Experts below are selected from a list of 2457 Experts worldwide ranked by ideXlab platform
Baojun Yang - One of the best experts on this subject based on the ideXlab platform.
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Simulation of Seismic-Prospecting random noise in the desert by a Brownian-motion-based parametric modeling algorithm
Comptes Rendus Geoscience, 2019Co-Authors: T. Zhong, Shuo Zhang, Baojun YangAbstract:Abstract Random noise has a negative impact on Seismic-Prospecting record processing. An important step to improve the methods aimed at the attenuation of random noise is to scientifically characterize the properties of the noise. Numerical modeling is useful to understand the nature of the random noise. In this study, we present a Brownian-motion-based parametric modeling algorithm for the simulation of Seismic-Prospecting random noise in the desert. The optimal Hurst exponent required to implement the method can be determined by comparing the spectral properties related to the noise data and the simulated results. The data used to analyze the properties of the noise were acquired in the Tarim Basin (Northwest of China). We verify the performance of the modeling algorithm by comparing the results obtained after the simulation with the real noise data in both the time domain and the spatio-temporal domain. The experimental results thus obtained prove the accuracy and efficiency of the proposed modeling algorithm. This study can be used as a basis to investigate the Seismic-Prospecting random noise characteristics and thus contribute to its mitigation.
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Statistical analysis of background noise in Seismic Prospecting
Geophysical Prospecting, 2015Co-Authors: T. Zhong, Pengfei Nie, Baojun YangAbstract:From a conventional viewpoint, Seismic-Prospecting background noise is usually regarded as the product of a stationary and Gaussian stochastic process. In this paper, we use statistical methods to investigate the properties of the land-Seismic-Prospecting background noise on stationarity, Gaussianity, power spectral density, and spatial correlation. We use and analyse the passive noise records collected by receiver arrays at different typical geological environments (desert, steppe, and mountainous regions). Differences exist in the statistical properties of the background noise from different geological environments, but we still find some common characteristics. It is shown that the background noise is not strictly stationary and has different stationary properties over different timescales.Most of the noise records appear to be a Gaussian process when examined over a period of about 20 s but are found to be non-Gaussian when examined over shorter periods of about 1 s. The background noise is a kind of colored noise, and its energy mainly concentrates in the low-frequency bands.We also find that the spatial correlation of the background noise is weak. The results of this paper provide a scientific understanding about the properties of Seismic-Prospecting background noise.
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A study on the stationarity and Gaussianity of the background noise in land-Seismic Prospecting
GEOPHYSICS, 2015Co-Authors: T. Zhong, Pengfei Nie, Baojun YangAbstract:ABSTRACTIn the denoising of Seismic Prospecting, background noise is often assumed to be stationary and Gaussian. However, this is not always appropriate for real Seismic data. We used statistical tests to assess the stationarity and Gaussianity of land Seismic data. The data we used for the analyses were passive noise records collected with receiver arrays in different land environments, e.g., deserts, steppes, and mountains. The results showed that the background noise was not strictly stationary, but locally stationary. The noise could be treated as a stationary series only in short time periods, whereas the stationarity became poor with increasing the time length of the noise records. By analyzing the behavior of noise data, we determined that the nonstationary noise always had more energy in the high-frequency band, which varied with the acquisition environments. The wind strength and the complexity of the environmental conditions also impacted the noise stationarity. Moreover, we found that noise in...
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LS-SVR with variant parameters and its practical applications for Seismic Prospecting data denoising
2008 IEEE International Symposium on Industrial Electronics, 2008Co-Authors: Xiaoying Deng, Dinghui Yang, Baojun YangAbstract:Signal denoising can be considered as a function regression problem. LS-SVR (least squares-support vector regression) based on Ricker wavelet kernel function is applied to the practical Seismic Prospecting data denoising in this paper. To adapt LS-SVR well to the practical Seismic data, the parameters including Ricker wavelet kernel parameter f and regularization parameter ? are selected automatically according to the features of data in the fixed window. The denoising experimental results for the theoretical and practical Seismic data show that the performance of Ricker wavelet LS-SVR with variant parameters outperforms the one with invariant parameters in terms of the retrieved waveform in time domain and spectrum range in frequency domain.
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Ricker wavelet LS-SVM and its parameters setting for Seismic Prospecting signals denoising
Second International Conference on Space Information Technology, 2007Co-Authors: Xiaoying Deng, Baojun YangAbstract:LS-SVM (Least Squares-Support Vector Machines) are applied to Seismic Prospecting signals denoising so as to suppress the stochastic noise in this paper. Firstly, we propose and prove a new admissible support vector kernel-Ricker wavelet kernel, which is superior to the popular RBF (radial basis function) kernel in terms of the waveform retrieved and SNR (Signal to Noise Ratio) gained when applied to the noise reduction of Seismic Prospecting signals. LS-SVM embed two tuning parameters which may diminish the overall performance of LS-SVM if not well chosen, so we investigate the selection of LS-SVM parameters including kernel parameter and regularization parameter, respectively. We can conclude that Ricker wavelet kernel parameter should be set as the predominant frequency of Seismic signal and regularization parameter γ can be accepted in a wide range. Our denoising experimental results show that the performance of Ricker wavelet LS-SVM using the aforementioned parameters setting outperforms Wiener filtering, median filtering and LS-SVM based on RBF kernel in terms of the definition of Seismic Prospecting event retrieved and SNR gained.
Li Yue - One of the best experts on this subject based on the ideXlab platform.
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Study of parameters setting for least square support vector machine based on Ricker wavelet kernel in the denoising applications of Seismic Prospecting signals
2007Co-Authors: Li YueAbstract:Parameters setting for LS-SVM used in the regression processing is a difficult problem at all times.This setting is affected on the type and amplitude of signals,the type of kernel function,intensity of noise and calculation precision,etc.The setting of SVM parameter and kernel parameter in the denoising applications of Seismic Prospecting signals is analyzed and discussed separately in this paper.The experimental results show that kernel parameter f can be selected as the predominate frequency of the Seismic Prospecting records,bigger instead of smaller if not estimated accurately,and the acceptable range of the selection of SVM parameter γ is very wide except too small.According to the above methods of parameters setting,the simulation experiments on the noisy Seismic Prospecting signals have been done.
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Support Vector Regression Based on Ricker Wavelet Kernel Function and Its Application to Seismic Prospecting Data Denoising
Journal of Jilin University, 2007Co-Authors: Li YueAbstract:Aiming at suppressing the strong stochastic noise in Seismic Prospecting data,support vector regression(SVR) is introduced.A new permitted support vector kernel function-Ricker wavelet kernel function is proposed and demonstrated.Based on the elementary idea of kernel mapping and the principle of structural risk minimization,SVR transforms the regression problem into a quadratic programming problem.The results of simulation experiments for single channel data or arbitrary channel of multi-channel data show the clearer event,the better wave shape and the higher SNR compared with the conventional convolution filter and common SVR based on RBF.So it is possible that SVR based on Ricker wavelet kernel function is applied to suppressing noise in Seismic Prospecting data.
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Influence of the variety of the apparent dominant frequency of Ricker wavelets on the detection effects of chaotic oscillator
2006Co-Authors: Li YueAbstract:For the Seismic Prospecting data with additive noise,it has been put up to deal with the data using the chaotic oscillator system.It is needed to study the influence on the detection effects,which is aroused by such as the level of integrity of the Seismic Prospecting event、the variety of the apparent dominant frequency of the Ricker wavelet.The findings: with the apparent dominant frequency of the Ricker wavelet rising,the detection SNR will get worse gradually,it is obvious that the variety of the SNR is slower during the portion of high frequency than during the portion of low frequency; in the usual range of Seismic Prospecting frequency,the SNR of the chaotic oscillator detection system could partially make up the amplitude attenuation aroused by the transmit of the Seismic wave;the results could provide help for detecting the event constituted by aberrant wavelets.
T. Zhong - One of the best experts on this subject based on the ideXlab platform.
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Simulation of Seismic-Prospecting random noise in the desert by a Brownian-motion-based parametric modeling algorithm
Comptes Rendus Geoscience, 2019Co-Authors: T. Zhong, Shuo Zhang, Baojun YangAbstract:Abstract Random noise has a negative impact on Seismic-Prospecting record processing. An important step to improve the methods aimed at the attenuation of random noise is to scientifically characterize the properties of the noise. Numerical modeling is useful to understand the nature of the random noise. In this study, we present a Brownian-motion-based parametric modeling algorithm for the simulation of Seismic-Prospecting random noise in the desert. The optimal Hurst exponent required to implement the method can be determined by comparing the spectral properties related to the noise data and the simulated results. The data used to analyze the properties of the noise were acquired in the Tarim Basin (Northwest of China). We verify the performance of the modeling algorithm by comparing the results obtained after the simulation with the real noise data in both the time domain and the spatio-temporal domain. The experimental results thus obtained prove the accuracy and efficiency of the proposed modeling algorithm. This study can be used as a basis to investigate the Seismic-Prospecting random noise characteristics and thus contribute to its mitigation.
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A Study of the Parametric Modeling Algorithm for the Seismic Prospecting Random Noise
Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining, 2016Co-Authors: T. Zhong, R.j. Song, B. J. Yang, Y Li, H.l. TianAbstract:Summary The existence of the random noise has a negative impact on the Seismic Prospecting records processing. An important step for improving attenuation methods is to scientifically characterize the properties of the Seismic random noise. The modeling research is meaningful for cognizing the nature of the Seismic random noise. In this study, we present a preliminary research of the parametric modeling for the Seismic Prospecting random noise. According the actual conditions of the Seismic Prospecting, a modeling algorithm based on fraction Brownian motion is proposed. By comparing the spectral properties between the noise data and its simulated results, the optimum parameter can be determined. We check the performance of the modeling algorithm by comparing the simulated record to the real noise data in terms of power spectral density and statistical moments. The experiments prove the appropriateness of our modeling algorithm.
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Linearity Analysis of the Random Noise in Land Seismic Prospecting
Near Surface Geoscience 2016 - 22nd European Meeting of Environmental and Engineering Geophysics, 2016Co-Authors: T. Zhong, R.j. Song, B. J. Yang, H.l. TianAbstract:The existence of the random noise is one of the main obstacles for getting higher-accuracy Seismic records. The investigation of the Seismic random noise properties is the pathway for improving the performance of noise attenuation algorithms. Here, we use the statistical testing methods to investigate the linearity of land-Seismic-Prospecting random noise, whereas the linearity, to some extent, can reflect the complexity of the generating process of the noise. The results show that the noise cannot be considered as a linear stochastic process. By analyzing the behaviors of the dataset, we obtain that the non-linear noise always have more energy in high frequency bands, and the energy distributions of the non-linear noise are disordered in high frequencies. Thus, the linearity of the random noise should be improved by attenuating the energy in high frequencies. We use an experiment to prove the correctness of our results. Based on the generating mechanism of the random noise, we also give a reasonable explanation for the results of our study. The findings have implications for future noise reduction and signal detection algorithms.
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A Study on the Stationarity of the Random Noise in Seismic Prospecting
77th EAGE Conference and Exhibition 2015, 2015Co-Authors: T. Zhong, Y. TianAbstract:In the denoising process of Seismic Prospecting, random noise is often assumed to be stationary. However, this is not always appropriate for real Seismic data. Here, using the passive noise records collected according to the requirements of the actual Seismic Prospecting, we use the statistical testing methods to investigate the stationarity of land-Seismic-Prospecting random noise. The results show that the noise can not be considered as a stationary stochastic process. By analyzing the behaviors of the dataset, we obtain that the non-stationary noise always have more energy in high frequency bands, and the energy distributions of the non-stationary noise are disordered in high frequencies. Thus, the stationarity of the random noise should be improved by attenuating the energy in high frequencies. We use an experiment to prove the correctness of our results. Based on the generating mechanism of the random noise, we also give a reasonable explanation. The findings have implications for future noise reduction and signal detection algorithms.
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Statistical analysis of background noise in Seismic Prospecting
Geophysical Prospecting, 2015Co-Authors: T. Zhong, Pengfei Nie, Baojun YangAbstract:From a conventional viewpoint, Seismic-Prospecting background noise is usually regarded as the product of a stationary and Gaussian stochastic process. In this paper, we use statistical methods to investigate the properties of the land-Seismic-Prospecting background noise on stationarity, Gaussianity, power spectral density, and spatial correlation. We use and analyse the passive noise records collected by receiver arrays at different typical geological environments (desert, steppe, and mountainous regions). Differences exist in the statistical properties of the background noise from different geological environments, but we still find some common characteristics. It is shown that the background noise is not strictly stationary and has different stationary properties over different timescales.Most of the noise records appear to be a Gaussian process when examined over a period of about 20 s but are found to be non-Gaussian when examined over shorter periods of about 1 s. The background noise is a kind of colored noise, and its energy mainly concentrates in the low-frequency bands.We also find that the spatial correlation of the background noise is weak. The results of this paper provide a scientific understanding about the properties of Seismic-Prospecting background noise.
Xiaoying Deng - One of the best experts on this subject based on the ideXlab platform.
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LS-SVR with variant parameters and its practical applications for Seismic Prospecting data denoising
2008 IEEE International Symposium on Industrial Electronics, 2008Co-Authors: Xiaoying Deng, Dinghui Yang, Baojun YangAbstract:Signal denoising can be considered as a function regression problem. LS-SVR (least squares-support vector regression) based on Ricker wavelet kernel function is applied to the practical Seismic Prospecting data denoising in this paper. To adapt LS-SVR well to the practical Seismic data, the parameters including Ricker wavelet kernel parameter f and regularization parameter ? are selected automatically according to the features of data in the fixed window. The denoising experimental results for the theoretical and practical Seismic data show that the performance of Ricker wavelet LS-SVR with variant parameters outperforms the one with invariant parameters in terms of the retrieved waveform in time domain and spectrum range in frequency domain.
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Ricker wavelet LS-SVM and its parameters setting for Seismic Prospecting signals denoising
Second International Conference on Space Information Technology, 2007Co-Authors: Xiaoying Deng, Baojun YangAbstract:LS-SVM (Least Squares-Support Vector Machines) are applied to Seismic Prospecting signals denoising so as to suppress the stochastic noise in this paper. Firstly, we propose and prove a new admissible support vector kernel-Ricker wavelet kernel, which is superior to the popular RBF (radial basis function) kernel in terms of the waveform retrieved and SNR (Signal to Noise Ratio) gained when applied to the noise reduction of Seismic Prospecting signals. LS-SVM embed two tuning parameters which may diminish the overall performance of LS-SVM if not well chosen, so we investigate the selection of LS-SVM parameters including kernel parameter and regularization parameter, respectively. We can conclude that Ricker wavelet kernel parameter should be set as the predominant frequency of Seismic signal and regularization parameter γ can be accepted in a wide range. Our denoising experimental results show that the performance of Ricker wavelet LS-SVM using the aforementioned parameters setting outperforms Wiener filtering, median filtering and LS-SVM based on RBF kernel in terms of the definition of Seismic Prospecting event retrieved and SNR gained.
H.l. Tian - One of the best experts on this subject based on the ideXlab platform.
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A Study of the Parametric Modeling Algorithm for the Seismic Prospecting Random Noise
Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining, 2016Co-Authors: T. Zhong, R.j. Song, B. J. Yang, Y Li, H.l. TianAbstract:Summary The existence of the random noise has a negative impact on the Seismic Prospecting records processing. An important step for improving attenuation methods is to scientifically characterize the properties of the Seismic random noise. The modeling research is meaningful for cognizing the nature of the Seismic random noise. In this study, we present a preliminary research of the parametric modeling for the Seismic Prospecting random noise. According the actual conditions of the Seismic Prospecting, a modeling algorithm based on fraction Brownian motion is proposed. By comparing the spectral properties between the noise data and its simulated results, the optimum parameter can be determined. We check the performance of the modeling algorithm by comparing the simulated record to the real noise data in terms of power spectral density and statistical moments. The experiments prove the appropriateness of our modeling algorithm.
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Linearity Analysis of the Random Noise in Land Seismic Prospecting
Near Surface Geoscience 2016 - 22nd European Meeting of Environmental and Engineering Geophysics, 2016Co-Authors: T. Zhong, R.j. Song, B. J. Yang, H.l. TianAbstract:The existence of the random noise is one of the main obstacles for getting higher-accuracy Seismic records. The investigation of the Seismic random noise properties is the pathway for improving the performance of noise attenuation algorithms. Here, we use the statistical testing methods to investigate the linearity of land-Seismic-Prospecting random noise, whereas the linearity, to some extent, can reflect the complexity of the generating process of the noise. The results show that the noise cannot be considered as a linear stochastic process. By analyzing the behaviors of the dataset, we obtain that the non-linear noise always have more energy in high frequency bands, and the energy distributions of the non-linear noise are disordered in high frequencies. Thus, the linearity of the random noise should be improved by attenuating the energy in high frequencies. We use an experiment to prove the correctness of our results. Based on the generating mechanism of the random noise, we also give a reasonable explanation for the results of our study. The findings have implications for future noise reduction and signal detection algorithms.