统计相关条件下降水及洪水预报误差相关分析

以三峡水库上游寸滩至万县区间降水预报误差和入库洪水预报误差相应数据为例,在探讨两者统计相关性的基础上,采用Frank、Gumbel、Clayton三种二元Copula连接函数分析了两预报误差的相关结构,以离差平方和最小为准则进行了Copula函数的选择,并与两预报误差独立情况下联合频率分布进行比较和分析。研究结果表明,降水预报误差和洪水预报误差的相关性对其二元联合分布有一定影响,同时,在两预报误差负相关条件下,其联合分布可做简化处理。本文研究结果可为水库预报调度风险管理提供决策参考。     

洪水预报模型输入与输出的误差分布及相关性分析

降雨预报信息作为洪水预报模型的输入,该信息的准确性直接影响洪水预报模型输出的准确性.为探究模型输入(降雨预报)误差与输出(洪水预报)误差之间的关系,以英那河流域为例,分析了不同雨量等级下,预报模型的输入误差与输出误差的分布规律,并定性分析了两种误差的相关关系.结果表明,降雨量等级若为无雨及小雨时,两种误差不相关;若为中...     

彝良-镇雄铅锌矿矿集区资源潜力分析

矿集区构造、地层、遥感数据等控矿因素结合统计和分析,建立地质综合信息数学信息模型,并据此确定信息量边界值,在矿集区内圈定3个找矿有利靶区,一个A级两个B级。     

长江上游径流对天生港站潮位影响浅析

采用调和分析法进行天生港站潮位预报,预报精度不高,经分析长江上游径流是影响其预报精度的主要因素之一.本文通过对上游径流分析,得出天生港站潮位预报误差随同期流量差值增加而增加,差值趋于0时误差亦趋于0;同期流量差值相同,误差随流量的增加而减少;潮位误差的符号与同期流量差值的符号相反,为天生港站潮位的实时预报修正提供了依据,提高了预报的合格率,也可为提高其它感潮河段潮位预报精度提供参考.     

三维磁场资料曲化平边界单元法的改进

边界单元法是磁场曲化平的一种原理简单而实用的数值计算方法,但该方法在处理大数据量的实际资料时,主要的困难是要求解一个大型、高阶的方程组,且边界值误差较大.对大型、高阶的方程利用小波压缩算法进行压缩和降阶处理,能节省时间和计算机内存,从而提高效率并发挥计算机的潜能.在保证足够精度的前提下,可以获得45%以上的压缩比.通过模型计算表明,该方法是可行的,能高效、精确处理磁场曲化平问题.     

前郭-扶余地区地下水流数值模拟

研究区位于松辽盆地中西部，含水层主要为第四纪地层，采用迦辽金有限单元法对区内开采层进行了数值模拟和预报，为区内小城镇发展的供水问题提供了开采方案。由于边界条件及源汇项等都是经过分析推测的，所以随着预报时间的增长，预报的可靠性也将降低。     

西北地区石炭纪古地理轮廓及沉积特征

本文所述西北地区,包括新疆、甘肃、青海北部及宁夏西南部。西自中苏边界,东至贺兰山、六盘山;北迄中蒙边界,南至昆仑山脉以北的广大区域。区内石炭纪地层具有较多的沉积类型和复杂的古地理景观以及独特的发展历史,与我国华北、华南地区比较有着显著的不同。      

北京市山区泥石流灾害的发育特征及预报方法探讨

北京山区泥石流灾害较为发育。泥石流分布地域广泛,但相对集中于部分乡镇、主干断裂构造带附近或几组断裂构造交汇部位、坚硬岩石分布区、末级和二级沟谷以及降雨高值区内,且多发生在7-8月份暴雨季节。受地形地貌、地质条件、降雨分布、土壤类型、气温条件以及植被覆盖程度等影响明显。对于泥石流的预报,目前主要依据的是临界雨量值。本文通过认真研究北京地区泥石流的发育规律,深入分析了泥石流的形成条件和影响因素,并在此基础上对北京地区泥石流预报方法进行了初步探讨。建立了综合考虑地形地貌、地质条件、土壤类型以及降雨情况等因素的判断公式,并就如何开展北京地区泥石流预报工作提出了建议。     

洪水预报过程的交互式修正技术研究

由于预报模型的局限性和实时信息的不完善,洪水预报过程存在许多误差,而基于图形交互式修正技术是消除预报误差的有效手段。分析了水文预报过程交互式修正技术在洪水预报工作中的重要性,介绍了过程拟合平滑技术和样条插值技术,基于此基础上研究实现了以橡皮筋形式交互式修正水文预报过程的技术,并应用于洪水预报系统中。研究实例表明,该技术使用方便,有效地提高了洪水预报精度。     

地球化学模式程序在铀矿找矿中的应用——以巴音戈壁盆地塔木素地区为例

首先简述了地球化学模式程序、与铀成矿有关的水文地球化学参数及区域地质、水文地球化学概况,并研究了区内水文地球化学环境。在此基础上,依据区内水质分析资料,运用地球化学模式程序(PHREEQC)计算了下白垩统地下水中沥青铀矿饱和指数(SI)、Eh反应条件边界值(Ehb,U)及Eh反应条件指数(RCIEh)等。认为该区局部地段在下白垩统地下水的深部存在Ehw﹤Ehb,U,RCIEh<0,SI>0的条件,即地下水处于过渡环境中,地下水中铀处于沉淀析出的饱和或过饱和状态,与岩石地球化学环境吻合性较好。因而在区内具有铀矿化的可能性,已被区内发现的工业铀矿化得到了证实。     

基于灰色系统与线性回归方法的水质预测

以北京市石景山区某地地下水监测点多年监测数据为例,进行适宜性预测方法的验证。其中,总硬度灰色预测值的平均相对误差为2.62%比线性预测低5.65个百分点;溶解性总固体线性预测值的平均相对误差为2.33%比灰色预测低0.61个百分点,与通过拟合度R~2值大小选取的最优预测方法一致,表明通过拟合度R~2值大小来选取合理的预测方法是一种便捷、合理的技术手段。     

永安地震前的地应力短临异常

本文介绍pyL-1型岩石应力仪观测地应力的异常变化,分析了异常与永安地震的关系,应力长时间的大幅度连续变化可能是较大地震前兆,异常极值左右摆动是临震前兆,本人据此在去年年终会商和今年会商时提出预报意见。     

Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils

This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd.     

测井约束地震岩性预测方法及应用——以汤原断陷达连河组SD2 段为例

为精细刻画汤原断陷达连河组的岩性展布, 运用测井约束地震岩性预测方法, 利用研究区丰富的井震及钻井资料, 拟合速度- 岩性间的函数关系, 经高精度层序地层约束后, 进行岩性预测。预测结果符合率达86% , 揭示了目的层岩性空间展布规律: 沉积厚度高值区和砂体厚度中心主要分布于控盆断裂一侧, 在研究区内主要分布于靠近盆缘断裂的东部凹陷带。     

系统滤波模型在滇池水质预测中的应用

根据滇池水深较浅，流场比较稳定等特点，对湖泊进行了单元划分，考虑计算误差和观测误差干扰的存在，建立了系统滤波模型，用吉尔算法求解微分方程，对ＢＯＤ，ＣＯＤ多点连续模拟预测，经用１９８８年实测资料检验，取得满意结果。     

A study of soft computing models for prediction of longitudinal wave velocity

Genetic algorithm (GA) and support vector machine (SVM) optimization techniques are applied widely in the area of geophysics, civil, biology, mining, and geo-mechanics. Due to its versatility, it is being applied widely in almost every field of engineering. In this paper, the important features of GA and SVM are discussed as well as prediction of longitudinal wave velocity and its advantages over other conventional prediction methods. Longitudinal wave measurement is an indicator of peak particle velocity (PPV) during blasting and is an important parameter to be determined to minimize the damage caused by ground vibrations. The dynamic wave velocity and physico-mechanical properties of rock significantly affect the fracture propagation in rock. GA and SVM models are designed to predict the longitudinal wave velocity induced by ground vibrations. Chaos optimization algorithm has been used in SVM to find the optimal parameters of the model to increase the learning and prediction efficiency. GA model also has been developed and has used an objective function to be minimized. A parametric study for selecting the optimized parameters of GA model was done to select the best value. The mean absolute percentage error for the predicted wave velocity (V) value has been found to be the least (0.258 %) for GA as compared to values obtained by multivariate regression analysis (MVRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and SVM.     

A new approach for crude oil price prediction based on stream learning

Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons.     

东营凹陷古近系岩性圈闭识别方法研究

针对东营凹陷岩性油气藏的勘探特点,在三级层序格架内识别出体系域,研究了主要岩性体的成因类型、分布特征及其时空配置关系,通过岩性油气藏储层模型的建立和地震预测模型参数的研究,提高了岩性圈闭预测评价和描述的精度.地质和地震预测技术研究的有机结合揭示了在高频可容纳空间变化条件下岩性体沉积作用的有序性及其边界条件,构成以地质规律为指导的岩性圈闭识别技术系列.     

沉降预测的非等步长灰色时变参数模型

根据灰色系统理论的信息处理原则,在灰色模型中引入时变参数,建立了沉降预测的非等步长灰色时变参数模型。模型充分考虑了预测系统的时变性和灰色性,在求解过程中,引入遗忘因子以修正预测结果,并根据后验方差比C确定遗忘因子λ的最优值,以提高预测精度。沉降预测实例表明：沉降预测的非等步长灰色时变参数模型具有较强的适用性,可降低沉降量的预测误差和提高沉降的预测精度。     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.