Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP)...Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.展开更多
After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve we...After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.展开更多
Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic...Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.展开更多
In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively est...In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.展开更多
Five wells of L oilfield in Bohai bay basin have drilled 10 - 15 meters thick oil layer in the Paleogene delta. Due to the deep-buried reservoir and the poor seismic performance, it is difficult to identify the reserv...Five wells of L oilfield in Bohai bay basin have drilled 10 - 15 meters thick oil layer in the Paleogene delta. Due to the deep-buried reservoir and the poor seismic performance, it is difficult to identify the reservoir genesis, and predict reservoir distribution. By analyzing core, well logging and seismic data, a stable mudstone section is selected as the correlation marker to establish a stratigraphic framework. The paleogeomorphology is reconstructed after decompaction correction and paleobathymetric analysis. Based on the differences of paleotopography and sedimentary facies, the study area mainly develops two delta systems: low gradient coarse-grain delta system and steep gradient delta-turbidite system. The favorable reservoir of low gradient coarse-grain delta, which is thick and has good lateral connectivity, mainly locates in the delta front. The favorable reservoir of steep gradient delta-turbidite system locates in the delta front and turbidite facies, and the delta front deposits are thin and have poor lateral connectivity. The boundary of delta front is first depicted on the basis of paleotopography. In combination with reservoir architecture and forward modeling analysis, the seismic attribute is then optimized to predict the distribution of favorable reservoir. Using this method, several sets of oil-bearing sandbodies have been drilled in L oilfield, and the prediction accuracy of reservoir distribution is proved to be high. This study demonstrates that the paleogeomorphology plays an important role in controlling the genesis and distribution of the delta reservoir and provides reference for the reservoir prediction in similar oilfields.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
Currently in Niu-zhuang sub-sag, the seismic reflection amplitude of the newly discovered turbidite sandstone is stronger in the third Segment. The main reason is that Calcareous components accounts for a large part a...Currently in Niu-zhuang sub-sag, the seismic reflection amplitude of the newly discovered turbidite sandstone is stronger in the third Segment. The main reason is that Calcareous components accounts for a large part and physical properties is relatively poor, which results in no corresponding relation between reservoir and seismic attributes, and effective reservoir is difficult to predict and describe. Therefore, using the method of geological statistics, we firstly study the distribution of calcareous matters, secondly study the contribution to seismic reflection amplitude made by Calcareous high impedance component;thirdly analyze its influence on actual seismic reflection amplitude and determine the lithology thickness of Calcareous via replacement forward modeling. At last, we characterize the reservoir using the amplitude of calcareous matters. It proves that the method of seismic-geological comprehensive prediction is reliable. It has good guidance for exploration and development of the calcareous sand lithologic reservoir in similar areas.展开更多
南阳凹陷黑龙庙地区砂砾岩体规模小,多期砂体叠置,沉积期次划分难,非均质性强、甜点区难以预测。为此,利用可视化技术对三维地震资料进行精细构造解析与古构造恢复,研究砂砾岩体成因。在对5口井层序地层划分基础上,依据砂组-砂体的沉积...南阳凹陷黑龙庙地区砂砾岩体规模小,多期砂体叠置,沉积期次划分难,非均质性强、甜点区难以预测。为此,利用可视化技术对三维地震资料进行精细构造解析与古构造恢复,研究砂砾岩体成因。在对5口井层序地层划分基础上,依据砂组-砂体的沉积旋回性与地震反射结构,划分砂砾岩体沉积期次;通过岩石物理分析与正演模拟明确砂砾岩体地球物理响应特征。通过井点处扇根、扇中、扇端地震反射特征与相同层位其他点处的地震反射特征进行相关分析,确定扇根、扇中、扇端的平面分布;实测砂砾岩体岩性、物性参数与波阻抗属性,交汇分析确定有效区分砂砾岩与泥岩的波阻抗属性的值域。依据高精度反演得到的波阻抗数据体,在砂砾岩体储层波阻抗值域及顶底反射层位控制下,求出有效储层厚度与分布,圈定砂砾岩体储层甜点区的范围。以此为依据在其甜点区上部署的HL1井日产油5.46 m 3,预测结果与实钻井一致。展开更多
在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol...在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol Based on Link Prediction and Energy Sensing,ELPOR).该协议综合考虑节点能量和各节点之间链路连接的概率,从潜在的候选转发集中选择一个中继节点,以实现能量的高效利用和数据的可靠传输。仿真结果表明,该协议能够有效均衡网络能耗、提高吞吐量和延长网络生存周期。展开更多
基金the University of Oklahoma(OU)Supercomputing Center for Education&Research(OSCER).
文摘Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.
基金supported by the NOAA (Grant Nos. NA16AOR4320115 and NA11OAR4320072)NSF (Grant No. AGS-1341878)
文摘After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.
基金Project(2013CB036004)supported by the National Basic Research Program of ChinaProject(51378510)supported by the National Natural Science Foundation of China
文摘Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.
基金jointly supported by the National Natural Science Foundation of China for Excellent Young Scholars (Grant No. 41522502)the National Program on Global Change and Air–Sea Interaction (Grant Nos. GASI-IPOVAI06 and GASI-IPOVAI-03)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2015BAC03B07)
文摘In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.
文摘Five wells of L oilfield in Bohai bay basin have drilled 10 - 15 meters thick oil layer in the Paleogene delta. Due to the deep-buried reservoir and the poor seismic performance, it is difficult to identify the reservoir genesis, and predict reservoir distribution. By analyzing core, well logging and seismic data, a stable mudstone section is selected as the correlation marker to establish a stratigraphic framework. The paleogeomorphology is reconstructed after decompaction correction and paleobathymetric analysis. Based on the differences of paleotopography and sedimentary facies, the study area mainly develops two delta systems: low gradient coarse-grain delta system and steep gradient delta-turbidite system. The favorable reservoir of low gradient coarse-grain delta, which is thick and has good lateral connectivity, mainly locates in the delta front. The favorable reservoir of steep gradient delta-turbidite system locates in the delta front and turbidite facies, and the delta front deposits are thin and have poor lateral connectivity. The boundary of delta front is first depicted on the basis of paleotopography. In combination with reservoir architecture and forward modeling analysis, the seismic attribute is then optimized to predict the distribution of favorable reservoir. Using this method, several sets of oil-bearing sandbodies have been drilled in L oilfield, and the prediction accuracy of reservoir distribution is proved to be high. This study demonstrates that the paleogeomorphology plays an important role in controlling the genesis and distribution of the delta reservoir and provides reference for the reservoir prediction in similar oilfields.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
文摘Currently in Niu-zhuang sub-sag, the seismic reflection amplitude of the newly discovered turbidite sandstone is stronger in the third Segment. The main reason is that Calcareous components accounts for a large part and physical properties is relatively poor, which results in no corresponding relation between reservoir and seismic attributes, and effective reservoir is difficult to predict and describe. Therefore, using the method of geological statistics, we firstly study the distribution of calcareous matters, secondly study the contribution to seismic reflection amplitude made by Calcareous high impedance component;thirdly analyze its influence on actual seismic reflection amplitude and determine the lithology thickness of Calcareous via replacement forward modeling. At last, we characterize the reservoir using the amplitude of calcareous matters. It proves that the method of seismic-geological comprehensive prediction is reliable. It has good guidance for exploration and development of the calcareous sand lithologic reservoir in similar areas.
文摘南阳凹陷黑龙庙地区砂砾岩体规模小,多期砂体叠置,沉积期次划分难,非均质性强、甜点区难以预测。为此,利用可视化技术对三维地震资料进行精细构造解析与古构造恢复,研究砂砾岩体成因。在对5口井层序地层划分基础上,依据砂组-砂体的沉积旋回性与地震反射结构,划分砂砾岩体沉积期次;通过岩石物理分析与正演模拟明确砂砾岩体地球物理响应特征。通过井点处扇根、扇中、扇端地震反射特征与相同层位其他点处的地震反射特征进行相关分析,确定扇根、扇中、扇端的平面分布;实测砂砾岩体岩性、物性参数与波阻抗属性,交汇分析确定有效区分砂砾岩与泥岩的波阻抗属性的值域。依据高精度反演得到的波阻抗数据体,在砂砾岩体储层波阻抗值域及顶底反射层位控制下,求出有效储层厚度与分布,圈定砂砾岩体储层甜点区的范围。以此为依据在其甜点区上部署的HL1井日产油5.46 m 3,预测结果与实钻井一致。
文摘在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol Based on Link Prediction and Energy Sensing,ELPOR).该协议综合考虑节点能量和各节点之间链路连接的概率,从潜在的候选转发集中选择一个中继节点,以实现能量的高效利用和数据的可靠传输。仿真结果表明,该协议能够有效均衡网络能耗、提高吞吐量和延长网络生存周期。
