统计检验抗差估计新方法研究

20世纪60年代数学家重新从广泛的意义上提出非最小二乘估计问题,并已在抗差估计中取得重要的进展。从1980年起,抗差估计理论开始被引入测量领域,并出现了许多方案。本文依据统计检验,结合IGG1区间划分的部分思想,提出了一种全新的定权思路,给出了一个简要数学模型。利用MATLAB编程,将本文提出的方法与经典的IGG1方法进行了比较,对它的抗差效果进行了总结分析,通过一个经典算例,表明新方法具有准确定位粗差的优点。     

Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of gran...     

基于遗传算法优化BP神经网络的玉米遥感估产方法

以内蒙古自治区开鲁县玉米作物为研究对象,将生育期内玉米遥感影像所提取的多种植被指数和实地采样点的测产数据作为训练值,利用BP(back propagation)神经网络和遗传算法优化BP(GA-BP)神经网络估产模型,得出网络预测的玉米产量数值。通过决定系数R 2和均方根误差RMSE,比较实测产量与预测产量之间的精度,BP神经网络模型R^2为0.8452,RMSE(%)为28.37;遗传算法优化BP神经网络模型R^2为0.9850,RMSE(%)为6.70,表明遗传算法优化BP神经网络估产模型具有一定可行性和可信度。     

直接基于位移的结构抗震设计理论研究进展

直接基于位移的抗震设计是实现基于性能抗震设计思想的一条有效途径,其理论主要包括三方面的内容,即直接基于位移的抗震设计方法、位移需求估计方法和目标位移的确定。本文在阅读了大量文献的基础上,对国内外在这三方面的研究进行了介绍,并对今后的研究方向进行了展望。     

Pushover-based seismic risk assessment and loss estimation of masonry buildings

A pushover-based seismic risk assessment and loss estimation methodology for masonry buildings is introduced. It enables estimation of loss by various performance measures such as the probability of exceeding a designated economic loss, the expected annual loss, and the expected loss given a seismic intensity. The methodology enables the estimation of the economic loss directly from the results of structural analysis, which combines pushover analysis and incremental dynamic analysis of an equivalent SDOF model. The use of the methodology is demonstrated by means of two variants of a three-storey masonry building both of which have the same geometry, but they are built, respectively, from hollow clay masonry (model H) and solid brick masonry (model S). The probability of collapse given the selected design earthquake corresponding to a return period of 475 years was found to be negligible for model H, which indicates the proper behaviour of such a structure when designed according to the current building codes. However, the corresponding probability of collapse of model S was very high (46%). The expected total loss given the design earthquake was estimated to amount to 28 000 € and 290 000 €, respectively, for models H and S. The expected annual loss per 100 m² of gross floor area was estimated to amount to 75 € and 191 €, respectively, for models H and S. For the presented examples, it was also observed that nonstructural elements contributed more than 50% of the total loss.     

一种实时场地校正方法及其在地震预警中的应用

地震预警系统对地震数据处理的实时性要求极高。其系统数据来源除布设在基岩的测震台站外,还有大量非基岩场地的强震动台站和地震烈度仪台站,其场地影响不容忽视。为了考虑震级估算和地震动场预测中的场地影响,需实时对各种场地条件下的地震波形进行校正。目前处理一般使用某个标量来表征场地放大效应。本文采用一种实时的场地校正方法,首先计算目标场地与参考场地的谱比,然后通过最小二乘法、双线性变换将谱比转化为因果递归的无限冲激响应(IIR)时域滤波器,之后可以应用该滤波器进行实时场地校正。该方法考虑了场地放大系数的频率依赖性,相比于标量校正提高了准确度。应用我国四川和日本部分强震动台站记录,验证并讨论了这种实时场地校正方法在地震预警中的应用效果。     

三峡澎溪河水-气界面温室气体模型估算及其敏感性分析

模型估算法是水-气界面温室气体通量监测的主要方法,所得成果也不胜枚举.然而监测过程中诸多环境因素会对最终结果产生不确定的影响.结合三峡库区澎溪河背景条件,利用模型估算法进行水-气界面温室气体通量(以CO_2为例)估算,并且采用修正Morris筛选法尝试分析模型估算法中各个参数对温室气体扩散通量(以CO_2为例)的局部敏感性.研究结果表明:利用模型估算法计算三峡澎溪河流域水-气界面温室气体通量具有较高的可行性和可靠性;风速、水温以及pH值会对监测结果产生影响,且风速越强、水温越高、pH值越小,CO_2扩散通量就越大;pH值是高灵敏参数,风速和水温是灵敏参数.在三峡库区澎溪河监测过程中更应注意pH值的精确性,每次采样前需校正仪器.     

Some remarks on Q-compensated sparse deconvolution without knowing the quality factor Q

The subsurface media are not perfectly elastic, thus anelastic absorption, attenuation and dispersion (aka Q filtering) effects occur during wave propagation, diminishing seismic resolution. Compensating for anelastic effects is imperative for resolution enhancement. Q values are required for most of conventional Q-compensation methods, and the source wavelet is additionally required for some of them. Based on the previous work of non-stationary sparse reflectivity inversion, we evaluate a series of methods for Q-compensation with/without knowing Q and with/without knowing wavelet. We demonstrate that if Q-compensation takes the wavelet into account, it generates better results for the severely attenuated components, benefiting from the sparsity promotion. We then evaluate a two-phase Q-compensation method in the frequency domain to eliminate Q requirement. In phase 1, the observed seismogram is disintegrated into the least number of Q-filtered wavelets chosen from a dictionary by optimizing a basis pursuit denoising problem, where the dictionary is composed of the known wavelet with different propagation times, each filtered with a range of possible values. The elements of the dictionary are weighted by the infinity norm of the corresponding column and further preconditioned to provide wavelets of different values and different propagation times equal probability to entry into the solution space. In phase 2, we derive analytic solutions for estimates of reflectivity and Q and solve an over-determined equation to obtain the final reflectivity series and Q values, where both the amplitude and phase information are utilized to estimate the Q values. The evaluated inversion-based Q estimation method handles the wave-interference effects better than conventional spectral-ratio-based methods. For Q-compensation, we investigate why sparsity promoting does matter. Numerical and field data experiments indicate the feasibility of the evaluated method of Q-compensation without knowing Q but with wavelet given.     

Non-point source pollution estimation in the Pingqiao River Basin,China, using a spatial hydrograph-separation approach

For efficient and targeted management, this study demonstrates a recently developed non-point source (NPS) pollution model for a year-long estimation in the Pingqiao River Basin (22.3 km²) in China. This simple but physically reasonable model estimates NPS export in terms of land use by reflecting spatial hydrological features and source runoff measurements under different land-use types. The NPS export was separately analysed by a distributed hydrological model, a spatial hydrograph-separation technique, and an empirical water quality sub-model. Simulation results suggest that 57 890 kg of total nitrogen (TN) and 1148 kg of total phosphorus (TP) were delivered. The results, validated with observed stream concentrations, show relative errors of 23.3% for TN and 47.4% for TP. Countermeasures for urban areas (5.3% of total area) were prioritized because of the high contribution rate to TN (14.1%) and TP (26.2%) which is caused by the high degree of runoff (8.5%) and pollution source.     

Evaporation modelling by heuristic regression approaches using only temperature data

Accurate estimation of pan evaporation (E_pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and E_pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly E_pan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.