This paper presents a new approach for attenuating coherent noise in 3D seismic data. An adaptive beamforming with generalized sidelobe canceller (GSC) design methodology is utilized here as a general form of linearly...This paper presents a new approach for attenuating coherent noise in 3D seismic data. An adaptive beamforming with generalized sidelobe canceller (GSC) design methodology is utilized here as a general form of linearly constrained adaptive beamforming structure. It consists of a fixed beamformer, and a signal-blocking matrix in front of an unconstrained adaptive beamformer.Considerationf of the complexity of the geometry for 3D seismic survey, the 3D beamforming with GSC technique is developed with two key points: (1) sorting along azimuth sections to simplify the relationship between traveltime and offset from 3D to 2D, and (2) dynamic binning scheme to avoid the possible poor folding in some azimuth sections. Both simulation result and real data example show that the newly developed 3D beamforming with GSC yields more credible results at a relative low cost, sufficient stability and good resolution.展开更多
Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new fil...Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain （t-f-x）. Then in the t-f-x domain apply Empirical Mode Decomposition （EMD） on each frequency slice and clear the Intrinsic Mode Functions （IMFs） that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.展开更多
The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
One of the main drawbacks of Digital Holography(DH)is the coherent nature of the light source,which severely corrupts the quality of holographic reconstructions.Although numerous techniques to reduce noise in DH have ...One of the main drawbacks of Digital Holography(DH)is the coherent nature of the light source,which severely corrupts the quality of holographic reconstructions.Although numerous techniques to reduce noise in DH have provided good results,holographic noise suppression remains a challenging task.We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms,block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions.The optimized joint action of these different image-denoising methods permits the removal of up to 98%of the noise while preserving the image contrast.The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH.Experimental validation is provided for both singlewavelength and multi-wavelength DH,and a comparison with the most used holographic denoising methods is performed.展开更多
We theoretically investigate optomechanical force sensing via precooling and quantum noise cancellation in two coupled cavity optomechanical systems. We show that force sensing based on the reduction of noise can be u...We theoretically investigate optomechanical force sensing via precooling and quantum noise cancellation in two coupled cavity optomechanical systems. We show that force sensing based on the reduction of noise can be used to dramatically enhance the force sensing and that the precooling process can effectively improve the quantum noise cancellation. Specifically, we examine the effect of optomechanical cooling and noise reduction on the spectral density of the noise of the force measurement; these processes can significantly enhance the performance of optomechanical force sensing, and setting up the system in the resolved sideband regime can lead to an optimization of the cooling processes in a hybrid system. Such a scheme serves as a promising platform for quantum back-action-evading measurements of the motion and a framework for an optomechanical force sensor.展开更多
The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of man...The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components.In this study,we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components.The aim is to classify three typical features of a structural component—squares,slots,and holes—into various categories based on their dimensional errors(i.e.,“high precision,”“pass,”and“unqualified”).Two different types of classification schemes have been considered in this study:those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure.The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model.Based on the experimental data collected during the milling experiments,the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters(i.e.,“static features”)and cutting-force data(i.e.,“dynamic features”).The average classification accuracy obtained using the proposed deep learning model was 9.55%higher than the best machine learning algorithm considered in this paper.Moreover,the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises.Hence,the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.展开更多
基金This research is sponsored by by China Natural Science Foundation (40274041), China National Petroleum Corporation (CNPC)Innovation Fund (2002CXKF-3)
文摘This paper presents a new approach for attenuating coherent noise in 3D seismic data. An adaptive beamforming with generalized sidelobe canceller (GSC) design methodology is utilized here as a general form of linearly constrained adaptive beamforming structure. It consists of a fixed beamformer, and a signal-blocking matrix in front of an unconstrained adaptive beamformer.Considerationf of the complexity of the geometry for 3D seismic survey, the 3D beamforming with GSC technique is developed with two key points: (1) sorting along azimuth sections to simplify the relationship between traveltime and offset from 3D to 2D, and (2) dynamic binning scheme to avoid the possible poor folding in some azimuth sections. Both simulation result and real data example show that the newly developed 3D beamforming with GSC yields more credible results at a relative low cost, sufficient stability and good resolution.
基金sponsored by the National Natural Science Foundation of China (Grant No. 41174114)the National Natural Science Foundation of China and China Petroleum & Chemical Corporation Co-funded Project (No. 40839905)
文摘Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain （t-f-x）. Then in the t-f-x domain apply Empirical Mode Decomposition （EMD） on each frequency slice and clear the Intrinsic Mode Functions （IMFs） that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.
文摘The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
基金supported by DATABENC_Progetto SNECS-PON03PE_00163_1 Social Network delle Entitàdei Centri Storici.
文摘One of the main drawbacks of Digital Holography(DH)is the coherent nature of the light source,which severely corrupts the quality of holographic reconstructions.Although numerous techniques to reduce noise in DH have provided good results,holographic noise suppression remains a challenging task.We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms,block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions.The optimized joint action of these different image-denoising methods permits the removal of up to 98%of the noise while preserving the image contrast.The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH.Experimental validation is provided for both singlewavelength and multi-wavelength DH,and a comparison with the most used holographic denoising methods is performed.
基金supported by the Arba Minch University Ethiopia,and the National Natural Science Foundation of China(Grant Nos.11574041,and 11475037)
文摘We theoretically investigate optomechanical force sensing via precooling and quantum noise cancellation in two coupled cavity optomechanical systems. We show that force sensing based on the reduction of noise can be used to dramatically enhance the force sensing and that the precooling process can effectively improve the quantum noise cancellation. Specifically, we examine the effect of optomechanical cooling and noise reduction on the spectral density of the noise of the force measurement; these processes can significantly enhance the performance of optomechanical force sensing, and setting up the system in the resolved sideband regime can lead to an optimization of the cooling processes in a hybrid system. Such a scheme serves as a promising platform for quantum back-action-evading measurements of the motion and a framework for an optomechanical force sensor.
基金This work was supported by the National Natural Science Foundation of China(Grant No.52005205).The authors declare that they have no known conflicts of interest that could have appeared to influence the work reported in this paper.
文摘The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components.In this study,we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components.The aim is to classify three typical features of a structural component—squares,slots,and holes—into various categories based on their dimensional errors(i.e.,“high precision,”“pass,”and“unqualified”).Two different types of classification schemes have been considered in this study:those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure.The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model.Based on the experimental data collected during the milling experiments,the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters(i.e.,“static features”)and cutting-force data(i.e.,“dynamic features”).The average classification accuracy obtained using the proposed deep learning model was 9.55%higher than the best machine learning algorithm considered in this paper.Moreover,the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises.Hence,the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.
