The Experts below are selected from a list of 115302 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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seismic exploration Random Noise on land modeling and application to Noise suppression
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Baojun YangAbstract:In seismic exploration, Random Noise suppression is one of the key problems in seismic data processing. For Random Noise attenuation, the most important thing is the understanding of seismic Random Noise generation and propagation. Seismic Random Noise is considered as temporal and spatial Random processes, and it can be analyzed only qualitatively for now, due to its high variability. In this paper, we classify seismic Random Noise sources by their generation factors and simulate the Random Noise of the desert in West China. According to Green’s function, it can be assumed that seismic Random Noise sources are point-like sources that are distributed around geophones. A seismic Random Noise record is taken as the superimposed wave field exited by all the independent sources in a homogeneous isotropy half-infinite surface. Based on the wind vibration theory and preliminary study about ambient vibrations, the Noise source functions are determined. We obtain the waveforms of different kinds of Noise by solving the inhomogeneous wave equations and analyze the characteristics qualitatively and quantitatively. The seismic synthetic record with a 1.6-s time and 250-m distances is obtained, and the characteristics are compared between the simulated and the real Noise record in time domain and space domain, respectively. The comparative results show the same characteristics of the simulated Noise and the real Noise, which demonstrates the feasibility of the proposed method. According to the Noise modeling, it is known that the near-field cultural Noise is the main component of the Random Noise in the desert, on the basis of which complex diffusion filtering is selected. The filtered results by complex diffusion filtering is compared with the results of time–frequency peak filtering, which is a popular filtering method of seismic Random Noise suppression in recent years. The comparative results show that complex diffusion filtering is more suitable for the Noise of the desert in the Tarim Basin. This result proves that seismic Random Noise modeling can provide the guidance for Noise attenuation. It lays a foundation for researching the propagation characteristics and better attenuation of seismic Random Noise in the future.
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seismic directional Random Noise suppression by radial trace time frequency peak filtering using the hurst exponent statistic
Geophysical Prospecting, 2016Co-Authors: Chao Zhang, Yue Li, Baojun YangAbstract:Radial-trace time–frequency peak filtering filters a seismic record along the radialtrace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial-trace time–frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often Random Noise whose energy is concentrated in certain directions; the Noise in these directions is correlative. We refer to this kind of Random Noise (that is distributed Randomly in time but correlative in the space) as directional Random Noise. Under radial-trace time–frequency peak filtering, the directional Random Noise will be treated as signal and enhanced when this Noise has same direction as the signal. Therefore, we need to identify the directional Random Noise before the filtering. In this paper, we test the linearity of signal and directional Random Noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional Random Noise is a Random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional Random Noise by the Hurst exponent values. The directional Random Noise can then be suppressed by using a long filtering window length during the radial-trace time– frequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional Random Noise and can effectively recover the reflected events.
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matching pursuit based spatial trace time frequency peak filtering for seismic Random Noise attenuation
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Yue Li, Baojun YangAbstract:Time-frequency peak filtering (TFPF) is an effective seismic Random Noise attenuation method at low signal-to-Noise ratio (SNR). However, the conventional TFPF is biased for seismic signals with high frequency. We propose a spatial-trace TFPF (ST-TFPF) algorithm for reducing Random Noise in seismic data and simultaneously the bias of TFPF. The proposed method takes into consideration the lateral coherence between the neighboring traces as constraint of TFPF. To reduce bias, this algorithm takes TFPF along seismic events. The first stage of the proposed method preliminarily identifies the position of seismic reflection events using matching pursuit. The second stage consists of analyzing time delay of neighboring traces to construct spatial traces along seismic events. The last stage of algorithm consists of encoding seismic data along the constructed spatial traces and detecting the pseudo-Wigner-Ville distribution peaks of the encoded signals to reduce Random Noise. We assess our method on the synthetic and field data. The results illustrate that the ST-TFPF extends the signal preserving ability of TFPF in a wider range of window length at low SNR. Furthermore, comparison with conventional TFPF and wavelet denoising method shows that our method outperforms the two methods in Random Noise attenuation and seismic signal enhancement.
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seismic Random Noise attenuation and signal preserving by multiple directional time frequency peak filtering
Comptes Rendus Geoscience, 2015Co-Authors: Chao Zhang, Yue Li, Baojun YangAbstract:Abstract Time-frequency peak filtering (TFPF) is an effective method for seismic Random Noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate Random Noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method.
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variable eccentricity hyperbolic trace tfpf for seismic Random Noise attenuation
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Yanan Tian, Baojun YangAbstract:Seismic Noise attenuation to improve signal-to-Noise ratio plays an important role in seismic data processing. In recent years, time-frequency peak filtering (TFPF) has been introduced and applied to seismic Random Noise attenuation successfully. However, in the conventional TFPF, the window length (WL) is fixed and used for all frequency components. As a consequence, serious loss of the effective components is unavoidable due to the inappropriate WL. The recently proposed radial-trace TFPF adapts radial-trace transform to reduce the dominant frequencies of the effective signals. Nevertheless, the radial traces with a fixed inclination angle have some limitations for bent reflection events. To resolve these shortcomings, this paper presents a novel variable-eccentricity hyperbolic-trace TFPF. In this novel method, the noisy record is first resampled along a family of spatial–temporal hyperbolic filtering traces of different bending degrees. In this way, the spatial correlation between the adjacent channels is taken into account, the linearity of the input signals is enhanced, and the estimation bias of the instantaneous frequency is reduced. Moreover, there is little difference between the reduced dominant frequencies. A fixed WL is suitable for all reduced dominant frequencies without distortion of the effective components. Finally, we evaluate the performance of our method on some synthetic records and field data. The experimental results illustrate that our proposed method attenuates Random Noise effectively and recovers the effective reflection events smoothly and more continuously compared with the other methods.
Yue Li - One of the best experts on this subject based on the ideXlab platform.
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removal of Random Noise in seismic data by time varying window length time frequency peak filtering
Acta Geophysica, 2016Co-Authors: Pengjun Yu, Yue Li, Ning WuAbstract:Time-frequency peak filtering (TFPF) is an effective tool for the removal of Random Noise and can be used to process seismic data with a low signal- to-Noise ratio. A crucial aspect of this algorithm is the choice of window length (WL) of the time-frequency distribution. Whereas a fixed WL cannot simultaneously preserve signal and attenuate Noise, timevarying WLs can achieve this goal. We propose a new method, L-DVV (delay vector variance), which successfully processes non-stationary signals by using the surrogate to measure the non-linearity of a time series. This method is sensitive to Random Noise and can accurately recover seismic signal masked by Noise. Since the linearity criterion also meets the unbiased estimation criterion of the TFPF algorithm, the L-DVV method can be used for time-varying WL TFPF processing. Analysis of synthetic and real seismic data shows that the time-varying WL TFPF algorithm is effective at removing Noise and recovering seismic signal.
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seismic directional Random Noise suppression by radial trace time frequency peak filtering using the hurst exponent statistic
Geophysical Prospecting, 2016Co-Authors: Chao Zhang, Yue Li, Baojun YangAbstract:Radial-trace time–frequency peak filtering filters a seismic record along the radialtrace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial-trace time–frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often Random Noise whose energy is concentrated in certain directions; the Noise in these directions is correlative. We refer to this kind of Random Noise (that is distributed Randomly in time but correlative in the space) as directional Random Noise. Under radial-trace time–frequency peak filtering, the directional Random Noise will be treated as signal and enhanced when this Noise has same direction as the signal. Therefore, we need to identify the directional Random Noise before the filtering. In this paper, we test the linearity of signal and directional Random Noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional Random Noise is a Random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional Random Noise by the Hurst exponent values. The directional Random Noise can then be suppressed by using a long filtering window length during the radial-trace time– frequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional Random Noise and can effectively recover the reflected events.
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seismic Random Noise attenuation based on adaptive time frequency peak filtering
Journal of Applied Geophysics, 2015Co-Authors: Xinhuan Deng, Yue Li, Qian ZengAbstract:Abstract Time–frequency peak filtering (TFPF) method uses a specific window with fixed length to recover band-limited signal in stationary Random Noise. However, the derivatives of signal such as seismic wavelets may change rapidly in some short time intervals. In this case, TFPF equipped with fixed window length will not provide an optimal solution. In this letter, we present an adaptive version of TFPF for seismic Random Noise attenuation. In our version, the improved intersection of confidence intervals combined with short-time energy criterion is used to preprocess the noisy signal. And then, we choose an appropriate threshold to divide the noisy signal into signal, buffer and Noise. Different optimal window lengths are used in each type of segments. We test the proposed method on both synthetic and field seismic data. The experimental results illustrate that the proposed method makes the degree of amplitude preservation raise more than 10% and signal-to-Noise (SNR) improve 2–4 dB compared with the original algorithm.
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matching pursuit based spatial trace time frequency peak filtering for seismic Random Noise attenuation
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Yue Li, Baojun YangAbstract:Time-frequency peak filtering (TFPF) is an effective seismic Random Noise attenuation method at low signal-to-Noise ratio (SNR). However, the conventional TFPF is biased for seismic signals with high frequency. We propose a spatial-trace TFPF (ST-TFPF) algorithm for reducing Random Noise in seismic data and simultaneously the bias of TFPF. The proposed method takes into consideration the lateral coherence between the neighboring traces as constraint of TFPF. To reduce bias, this algorithm takes TFPF along seismic events. The first stage of the proposed method preliminarily identifies the position of seismic reflection events using matching pursuit. The second stage consists of analyzing time delay of neighboring traces to construct spatial traces along seismic events. The last stage of algorithm consists of encoding seismic data along the constructed spatial traces and detecting the pseudo-Wigner-Ville distribution peaks of the encoded signals to reduce Random Noise. We assess our method on the synthetic and field data. The results illustrate that the ST-TFPF extends the signal preserving ability of TFPF in a wider range of window length at low SNR. Furthermore, comparison with conventional TFPF and wavelet denoising method shows that our method outperforms the two methods in Random Noise attenuation and seismic signal enhancement.
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seismic Random Noise attenuation and signal preserving by multiple directional time frequency peak filtering
Comptes Rendus Geoscience, 2015Co-Authors: Chao Zhang, Yue Li, Baojun YangAbstract:Abstract Time-frequency peak filtering (TFPF) is an effective method for seismic Random Noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate Random Noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method.
Ram M Narayanan - One of the best experts on this subject based on the ideXlab platform.
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ultrawideband Random Noise radar design for through wall surveillance
IEEE Transactions on Aerospace and Electronic Systems, 2010Co-Authors: Ram M NarayananAbstract:We have developed an ultrawideband (UWB) Random Noise radar for through-wall surveillance applications. The operating frequency is in the ultrahigh frequency range, and the entire system is built around the concept of software defined radio. The radar receiver performance is statistically evaluated using both simulation studies and actual measurement results. We also discuss the phenomena of interference level and radar cross section (RCS) of the human target using the receiver operating characteristics (ROC). Experimental results presented show that Random Noise radars are useful for detecting and tracking humans obscured by building walls.
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through wall ranging and imaging using uwb Random Noise waveform system design considerations and preliminary experimental results
IEEE Antennas and Propagation Society International Symposium, 2009Co-Authors: Pinheng Chen, Ram M Narayanan, Chiehping Lai, Alexander A DavydovAbstract:In this work, a portable and real-time through-wall Random Noise radar is presented. The system is inherently covert and is immune from interference and jamming since a Random Noise waveform is used. By combining mature cross-correlation and SAR algorithm with FPGA-based receiver technology, the system is able to perform real-time range detection as well as imaging of targets, including human beings, behind a wall.
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doppler visibility of coherent ultrawideband Random Noise radar systems
IEEE Transactions on Aerospace and Electronic Systems, 2006Co-Authors: Ram M NarayananAbstract:Random Noise radar has recently been used in a variety of imaging and surveillance applications. These systems can be made phase coherent using the technique of heterodyne correlation. Phase coherence has been exploited to measure Doppler and thereby the velocity of moving targets. The Doppler visibility, i.e., the ability to extract Doppler information over the inherent clutter spectra, is constrained by system parameters, especially the phase Noise generated by microwave components. Our paper proposes a new phase Noise model for the heterodyne mixer as applicable for ultrawideband (UWB) Random Noise radar and for the local oscillator in the time domain. The Doppler spectra are simulated by including phase Noise contamination effects and compared with our previous experimental results. A genetic algorithm (GA) optimization routine is applied to synthesize the effects of a variety of parameter combinations to derive a suitable empirical formula for estimating the Doppler visibility in dB. According to the phase Noise analysis and the simulation results, the Doppler visibility of UWB Random Noise radar depends primarily on the following parameters: 1) the local oscillator (LO) drive level of the receiver heterodyne mixer, 2) the saturation current in the receiver heterodyne mixer, 3) the bandwidth of the transmit Noise source, and 4) the target velocity. Other parameters such as the carrier frequency of the receiver LO and the loaded quality factor of the LO have a small effect over the range of applicability of the model and are therefore neglected in the model formulation. The Doppler visibility curves generated from this formula match the simulation results very well over the applicable parameter range within 1 dB. Our model may therefore be used to quickly estimate the Doppler visibility of Random UWB Noise radars for trade-off analysis
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generalised wideband ambiguity function of a coherent ultrawideband Random Noise radar
IEE Proceedings - Radar Sonar and Navigation, 2003Co-Authors: Muhammad Dawood, Ram M NarayananAbstract:A coherent ultrawideband (UWB) Random Noise radar system has been developed and field tested at the University of Nebraska–Lincoln (UNL). A heterodyne correlation technique based on a time-delayed and frequency-shifted replica of the transmit waveform is used to inject coherence within this system. The radar's combined range and range rate resolution characteristics were investigated using the generalised wideband ambiguity function. As in the narrowband Random Noise waveform case, range and range rate resolutions can be controlled independently, the former being inversely related to the transmit bandwidth, while the latter is inversely related to the bandwidth of the integrating filter. It is also shown that UWB waveforms are not suitable for accurate range rate estimation due to the extended Doppler-spread parameter, i.e. the product of the transmit bandwidth and the target range rate, unless the correlator is matched in the delay rate as well.
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principles and applications of coherent Random Noise radar technology
SPIE's First International Symposium on Fluctuations and Noise, 2003Co-Authors: Ram M NarayananAbstract:Random Noise radar is rapidly emerging as a promising technique for high-resolution probing and imaging of obscured objects and interfaces. The University of Nebraska-Lincoln has developed and field-tested coherent ultra wideband polarimetric Random Noise radar systems that show great promise in their ability to estimate Doppler and image target and terrain features. Theoretical studies and extensive field tests using these systems confirm their ability to respond to and utilize phase information from the received signals. This paper summarizes our recent developments in coherent Random Noise radar imaging and discusses future research directions in this area.
Yangkang Chen - One of the best experts on this subject based on the ideXlab platform.
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deep learning seismic Random Noise attenuation via improved residual convolutional neural network
IEEE Transactions on Geoscience and Remote Sensing, 2021Co-Authors: Liuqing Yang, Wei Chen, Hang Wang, Yangkang ChenAbstract:Because a high signal-to-Noise ratio (SNR) is beneficial to the subsequent processing procedures, the Noise attenuation is important. We propose an adaptive Random Noise attenuation framework based on convolutional neural networks (CNNs). The framework transforms the target function from effective signal learning to Noise learning through residual learning, so as to improve the training efficiency. After sufficient training, the network transfers the learned seismic data features using a large synthetic data set to the testing of complex field data with unknown Noise levels and, thus, attenuates the Noise in an unsupervised way. Unsupervised Noise reduction requires certain representativeness of the training data and a sufficient amount of training data sets. In the network architecture, we introduce residual learning and batch normalization (BN) to reduce the training parameters of the network, thereby shortening the time for feature learning. The activation function with leakage correction function can effectively retain negative information, and its combination with the double convolutional residual block can enhance the generalization ability and feature extraction performance of the network. In the test of synthetic data and complex field data with unknown Noise levels, by comparing the Noise reduction results of some classic denoising algorithms, the adaptive CNN proposed in this article can more effectively attenuate the Noise and reconstruct the seismic waveform.
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damped multichannel singular spectrum analysis for 3d Random Noise attenuation
Geophysics, 2016Co-Authors: Weilin Huang, Yangkang Chen, Runqiu Wang, Shuwei GanAbstract:ABSTRACTMultichannel singular spectrum analysis (MSSA) is an effective algorithm for Random Noise attenuation in seismic data, which decomposes the vector space of the Hankel matrix of the noisy signal into a signal subspace and a Noise subspace by truncated singular value decomposition (TSVD). However, this signal subspace actually still contains residual Noise. We have derived a new formula of low-rank reduction, which is more powerful in distinguishing between signal and Noise compared with the traditional TSVD. By introducing a damping factor into traditional MSSA to dampen the singular values, we have developed a new algorithm for Random Noise attenuation. We have named our modified MSSA as damped MSSA. The denoising performance is controlled by the damping factor, and our approach reverts to the traditional MSSA approach when the damping factor is sufficiently large. Application of the damped MSSA algorithm on synthetic and field seismic data demonstrates superior performance compared with the conve...
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application of variational mode decomposition in Random Noise attenuation and time frequency analysis of seismic data
78th EAGE Conference and Exhibition 2016, 2016Co-Authors: W Liu, Yangkang Chen, Siyuan Cao, D ZhangAbstract:We introduce a novel approach for both Random Noise attenuation and time-frequency analysis of seismic data. The method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual Noise in the modes and it can avoid redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) method. Moreover, The VMD is an adaptive signal decomposition technique, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions. This new tool is based on a solid mathematical foundation and can obtain a well-defined time-frequency representation, which is more robust than the empirical mode decomposition (EMD) based decomposition approaches, such as the EEMD and CEEMD, which still remains empirically. We apply the VMD algorithm to Random Noise attenuation of seismic data by summarizing the low-frequency components after VMD. The application on field data demonstrates the potential of the proposed approach in highlighting geological characteristics (e.g. faults) effectively. All the performances of the VMD based approach are compared with those from the CEEMD based approach.
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Random Noise attenuation using local signal and Noise orthogonalization
Geophysics, 2015Co-Authors: Yangkang Chen, Sergey FomelAbstract:ABSTRACTWe have developed a novel approach to attenuate Random Noise based on local signal-and-Noise orthogonalization. In this approach, we first removed from a seismic section using one of the conventional denoising operators and then applied a weighting operator to the initially deNoised section to predict the signal-leakage energy, as well as retrieve it from the initial Noise section. The weighting operator was obtained by solving a least-squares minimization problem via shaping regularization with a smoothness constraint. Next, the initially deNoised section and the retrieved signal were combined to form the final deNoised section. The proposed denoising approach corresponded to orthogonalizing the initially deNoised signal and Noise in a local manner. We evaluated the denoising performance using local similarity. To test the orthogonalization property of the estimated signal and Noise, we calculated the local similarity map between the deNoised signal section and removed Noise section. Low values o...
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Random Noise attenuation using local signal and Noise orthogonalization
77th EAGE Conference and Exhibition 2015, 2015Co-Authors: Yangkang Chen, Sergey FomelAbstract:We propose a novel approach to attenuate Random Noise based on local signal-and-Noise orthogonalization. In this approach, we first remove Noise using one of the conventional denoising operators, and then apply a weighting operator to the Noise section in order to retrieve additional components of the useful signal. Next, the initially deNoised section and the retrieved signal are combined to form the final deNoised section. The weighting operator is obtained by solving a least-squares minimization problem via shaping regularization with a local-smoothness constraint. The proposed denoising approach corresponds to orthogonalizing the initially deNoised signal and Noise in a local manner. Field data example demonstrates an excellent performance of the proposed approach.
Hongbo Lin - One of the best experts on this subject based on the ideXlab platform.
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varying window length tfpf in high resolution radon domain for seismic Random Noise attenuation
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Guanghai Zhuang, Yanping Liu, Hongbo LinAbstract:The time–frequency peak filtering (TFPF) algorithm is an effective method for seismic Random Noise attenuation. The conventional TFPF filters seismic data only along the channel direction, ignoring the spatial characteristics of the reflection events, which results in the loss of directional information. In order to improve the filtering performance of TFPF, we adopt a Radon transform to implement spatiotemporal 2-D TFPF, which is doing TFPF in the Radon domain. Since this method takes the spatial correlation of the reflection events into account, it could extract the reflection events better than the conventional TFPF. As the conventional Radon transform may produce a smearing phenomenon, we apply an improved least squares high-resolution Radon transform to assist TFPF in this letter. Thus, the events can be highly focused to energy points in different positions of the Radon domain. Then we identify the signal parts and process them by TFPF with short window length (WL). The other parts are considered as Noise, and we process them by TFPF with long WL. By virtue of this new method, we can preserve the valid signal better and suppress the Random Noise more effectively. Through experiments on the synthetic seismic records and the field seismic data, the new method possesses a superior performance in Random Noise attenuation and seismic event preservation compared with the conventional TFPF and Radon TFPF methods.
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an amplitude preserved time frequency peak filtering based on empirical mode decomposition for seismic Random Noise reduction
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Yanping Liu, Hongbo LinAbstract:Time-frequency peak filtering (TFPF) is a classical filtering method in time-frequency domain. It applies Wigner-Ville distribution to estimate the instantaneous frequency of an analytical signal. There is a pair of contradiction in this method, i.e., selecting a short window length may lead to good preservation for signal amplitude but bad Random Noise reduction whereas selecting a long window length may lead to serious attenuation for signal amplitude but effective Random Noise reduction. In order to make a good tradeoff between valid signal amplitude preservation and Random Noise reduction, we adopt empirical mode decomposition (EMD) to improve the TFPF results. The new idea is to utilize the decomposition characteristic of EMD which decomposes a signal to several modes from high to low frequency and to take advantage of the time-frequency filtering characteristic of TFPF which can recognize the valid signal component in the time-frequency plane in order to achieve effective Random Noise reduction together with good amplitude preservation. Through some experiments on synthetic seismic models and field seismic records, we show the better performance of the new method compared with the conventional TFPF.
