Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., t...Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber- Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.展开更多
Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability ...Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.展开更多
The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework t...The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.展开更多
In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is ...In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.展开更多
Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and ideal...Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.展开更多
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy wit...Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.展开更多
Brittleness of rock plays a significant role in exploration and development of shale gas reservoirs. Young＇s modulus and Poisson＇s ratio are the key param- eters for evaluating the rock brittleness in shale gas expl...Brittleness of rock plays a significant role in exploration and development of shale gas reservoirs. Young＇s modulus and Poisson＇s ratio are the key param- eters for evaluating the rock brittleness in shale gas exploration because their combination relationship can quantitatively characterize the rock brittleness. The high- value anomaly of Young＇s modulus and the low-value anomaly of Poisson＇s ratio represent high brittleness of shale. The technique of pre-stack amplitude variation with angle inversion allows geoscientists to estimate Young＇s modulus and Poisson＇s ratio from seismic data. A model constrained basis pursuit inversion method is proposed for stably estimating Young＇s modulus and Poisson＇s ratio. Test results of synthetic gather data show that Young＇s modulus and Poisson＇s ratio can be estimated reasonably. With the novel method, the inverted Young＇s modulus and Poisson＇s ratio of real field data focus the layer boundaries better, which is helpful for us to evaluate the brittleness of shale gas reservoirs. The results of brittleness evaluation show a good agreement with the results of well interpretation.展开更多
Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring...Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.展开更多
The middle pulse repetition frequency（MPRF）and high pulse repetition frequency（HPRF）modes are widely adopted in airborne pulse Doppler（PD）radar systems,which results in the problem that the range measurement of ...The middle pulse repetition frequency（MPRF）and high pulse repetition frequency（HPRF）modes are widely adopted in airborne pulse Doppler（PD）radar systems,which results in the problem that the range measurement of targets is ambiguous.The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio（SNR）is high enough.In this paper,a multiple model particle flter（MMPF）based track-beforedetect（TBD）method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment.By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number（PIN）as components of the target state vector,and modeling the incremental variable of the PIN as a three-state Markov chain,the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state fltering problem.At last,the hybrid fltering problem is implemented by a MMPF-based TBD method in the Bayesian framework.Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously,and succeeds in detecting and tracking weak targets with the range ambiguous radar.Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis（MH）method in low-SNR environment.展开更多
Locating unknown emission sources in turbulent environments is a challenging yet crucial task,particularly in emergency response scenarios.Existing studies have developed information-theoretic approaches to fuse inter...Locating unknown emission sources in turbulent environments is a challenging yet crucial task,particularly in emergency response scenarios.Existing studies have developed information-theoretic approaches to fuse inter-mittent information collected by mobile sensors regarding the sources.This fused information is then used to support source-term estimation(STE)in various search algorithms.Among these,the cognitive strategy-a promising information-driven search algorithm-leverages a reward-based action selection mechanism to bal-ance exploration and exploitation during each search step.However,this mechanism is hampered by a high computational load and rigid search trajectories,limiting its application in real-world systems.To address these issues,this paper proposes a novel information-driven search method called Clutaxis,based on a global explo-ration and exploitation tradeoff principle.Specically,a particle filter is leveraged to maintain the STE.After projecing the particle flter samples onto a 2D search scene,the density-based spatial clustering of applications with noise(DBSCAN)algorithm is used to extract the density information of the samples,which is then used to construct a belief source area(BSA).By leveraging the uncertainty of the BSA,Clutaxis adopts explorative or exploitative actions with no restrictions on motion direction.Through dedicated simulations,the experimental results demonstrate the robustness of Clutaxis to key parameters and its advantages in computational complexity and search performance compared to two state-of-the-art algorithms(Infotaxis and Entrotaxis)and two Clutaxis variants(Clutaxis ER and Clutaxis ED).展开更多
基金supported by the National Basic Research Program of China(973 Program)(No.2013CB228603)National Science and Technology major projects(No.2011ZX05024 and 2011ZX05010)the National Natural Science Foundation of China(No.41174119)
文摘Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber- Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.
基金Project(71371182) supported by the National Natural Science Foundation of China
文摘Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.
基金Financial support for this work,provided by the National Natural Science Foundation of China(No.60974126)the Natural Science Foundation of Jiangsu Province(No.BK2009094)
文摘The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.
基金Project supported by the National Natural Science Foundation of China(Grant No.60504033)
文摘In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.
基金supported by the National Science and Technology Major Project(No.2011 ZX05007-006)the 973 Program of China(No.2013CB228604)the Major Project of Petrochina(No.2014B-0610)
文摘Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.
基金the sponsorship of the Key Technology for Geophysical Prediction of Ultra-Deep Carbonate Reservoirs(P24240)the National Natural Science Foundation of China(U24B2020)the National Science and Technology Major Project of China for New Oil and Gas Exploration and Development(Grant No.2024ZD1400102)。
文摘Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
基金the sponsorship of the National ‘‘973 Program’’ of China (2013CB228604)the National Grand Project for Science and Technology (2011ZX05030004-002)+6 种基金China Postdoctoral Science Foundation (2014M550379)Natural Science Foundation of Shandong (2014BSE28009)Science Foundation for Post-doctoral Scientists of Shandong (201401018)Science Foundation for Post-doctoral Scientists of QingdaoScience Foundation from SINOPEC Key Laboratory of Geophysics (33550006-14-FW2099-0038)the support of the Australian and Western Australian governments and the North West Shelf Joint Venture partnersthe Western Australian Energy Research Alliance (WA:ERA)
文摘Brittleness of rock plays a significant role in exploration and development of shale gas reservoirs. Young＇s modulus and Poisson＇s ratio are the key param- eters for evaluating the rock brittleness in shale gas exploration because their combination relationship can quantitatively characterize the rock brittleness. The high- value anomaly of Young＇s modulus and the low-value anomaly of Poisson＇s ratio represent high brittleness of shale. The technique of pre-stack amplitude variation with angle inversion allows geoscientists to estimate Young＇s modulus and Poisson＇s ratio from seismic data. A model constrained basis pursuit inversion method is proposed for stably estimating Young＇s modulus and Poisson＇s ratio. Test results of synthetic gather data show that Young＇s modulus and Poisson＇s ratio can be estimated reasonably. With the novel method, the inverted Young＇s modulus and Poisson＇s ratio of real field data focus the layer boundaries better, which is helpful for us to evaluate the brittleness of shale gas reservoirs. The results of brittleness evaluation show a good agreement with the results of well interpretation.
文摘Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.
基金supported by the National Natural Science Foundation of China(Nos.61179018,61102165,61002006,61102167)Aeronautical Science Foundation of China(No.20115584006)Special Foundation Program for Mountain Tai Scholars
文摘The middle pulse repetition frequency（MPRF）and high pulse repetition frequency（HPRF）modes are widely adopted in airborne pulse Doppler（PD）radar systems,which results in the problem that the range measurement of targets is ambiguous.The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio（SNR）is high enough.In this paper,a multiple model particle flter（MMPF）based track-beforedetect（TBD）method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment.By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number（PIN）as components of the target state vector,and modeling the incremental variable of the PIN as a three-state Markov chain,the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state fltering problem.At last,the hybrid fltering problem is implemented by a MMPF-based TBD method in the Bayesian framework.Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously,and succeeds in detecting and tracking weak targets with the range ambiguous radar.Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis（MH）method in low-SNR environment.
基金supported in part by the National Natural Science Foundation of China under Grant 62173337,72071207,21808181the National Social Science Fund of China under Grant 17CGL047。
文摘Locating unknown emission sources in turbulent environments is a challenging yet crucial task,particularly in emergency response scenarios.Existing studies have developed information-theoretic approaches to fuse inter-mittent information collected by mobile sensors regarding the sources.This fused information is then used to support source-term estimation(STE)in various search algorithms.Among these,the cognitive strategy-a promising information-driven search algorithm-leverages a reward-based action selection mechanism to bal-ance exploration and exploitation during each search step.However,this mechanism is hampered by a high computational load and rigid search trajectories,limiting its application in real-world systems.To address these issues,this paper proposes a novel information-driven search method called Clutaxis,based on a global explo-ration and exploitation tradeoff principle.Specically,a particle filter is leveraged to maintain the STE.After projecing the particle flter samples onto a 2D search scene,the density-based spatial clustering of applications with noise(DBSCAN)algorithm is used to extract the density information of the samples,which is then used to construct a belief source area(BSA).By leveraging the uncertainty of the BSA,Clutaxis adopts explorative or exploitative actions with no restrictions on motion direction.Through dedicated simulations,the experimental results demonstrate the robustness of Clutaxis to key parameters and its advantages in computational complexity and search performance compared to two state-of-the-art algorithms(Infotaxis and Entrotaxis)and two Clutaxis variants(Clutaxis ER and Clutaxis ED).
