Wave steepness retrieved from scatterometer data in a genetic algorithm

Wave steepness is an important characteristic of a high sea state, and is widely applied on wave propagations at ports, ships, offshore platforms, and CO2 circulation in the ocean. Obtaining wave steepness is a difficult task that depends heavily on theoretical research on wavelength distribution and direct observations. Development of remote-sensing techniques provides new opportunities to study wave steepness. At present, two formulas are proposed to estimate wave steepness from QuikSCAT and ERS-1/2 scatterometer data. We found that wave steepness retrieving is not affected by radar band, and polarization method, and that relationship of wave steepness with radar backscattering cross section is similar to that with wind. Therefore, we adopted and modified a genetic algorithm for relating wave steepness with radar backscattering cross section. Results show that the root-mean-square error of the wave steepness retrieved is 0.005 in two cases from ERS-1/2 scatterometer data and from QuikSCAT scatterometer data.     

��ɽ���¥̽�۹��͹���꼰��Թŵ����¼���ָʾ����

?????????(4??11 ??m)???????????????SMAR????????????????1976????????????￥????????е???????????????о?????????????????????????????????????????????????????????????????????????????????????????????????о???????????????50~7.16 ka???????????????????????????????????????????????????????????????????????????????????С???0.03 m/ka??????????0.6 m??????????1976???????????ɡ?75~50 ka??????????~50 ka????????????????????~0.1 m/ka??????ζ?????????????????????????????????~0.2 m/ka????????о???????????????仯????????????????????????????     

���ڵڶ�����Բ���ֵ������߻��������·���

????????????????????????????????????????????????????·??????÷?????黯γ??????????????????????????????????????????????????????????????????????????????????????t??????     

��Ͽ����ˮλ��ϫ�����뺬ˮ�������ϵ�о�

???????????????????????????????????????????λ??????????????????????????????????????????????????????????￡????λ????仯???????????????????????????????????????????????????????????????仯??????????????????????????????????????????й??     

变面积差动电容式传感器的研究

介绍一款变面积式差动电容传感器.该传感器借助于两路相位相差180°的激励信号源作用于两固定极,使动摆片与定摆片之间形成电场.动摆片在进行摆动的过程中,在左右两固定摆中的面积会发生相应的变化,进而会产生电容量的改变.动摆片摆动角度的大小与动摆片在定片中的面积以及电容量的大小呈线性比例关系.通过交流电桥法可测量出该电容传感器的电容量的变化所对应的电压输出,并推算出动摆片位移角度的大小.实验验证了该设计的可行性.     

华北地区定点台站形变资料分析

对华北地区定点台站形变观测资料,采用傅立叶去年周期和线性拟合的方法进行处理.结果表明,华北地区现阶段的变化未形成主体异常时段,反映出断层未进入强烈活动阶段;首都圈地区跨断层资料异常数量不多,没有形成同步异常,异常变化幅值也不是很大,中短临地震异常不明显.     

基于GIS的矿产预测地质解译空间信息集成

利用我国海量地质标准基础数据库中的数字地质图和矿产图,通过基于GIS的地质解译空间集成地质信息,将其用于综合信息矿产预测。以地质解译系统对内蒙大兴安岭南段1∶20万成矿预测的应用为案例,阐述地质信息的空间提取与集成过程：首先在建立地质字典库实现地质空间信息共享的基础上,通过矿化密集区对地质模型的分类图层进行空间分析,建立地质成矿空间信息库和图库;然后,基于典型矿床圈定模型单元,通过模型单元与地质成矿空间信息库和图库的空间分析,建立地质找矿模型;最后,基于地质单元对地质成矿空间信息库和图库的二次空间集成,完成预测模型的地质空间信息提取与集成。将本方法应用在银矿案例的综合信息矿产预测靶区评价上,得到可供进一步查证的新增靶区比已知靶区增加了近5倍。     

滇西芒棒盆地芒棒组碳质粉砂岩的工程地质特性

滇西芒棒盆地是在上新世喜山运动后期高黎贡山强烈隆升过程中形成的近SN向的断陷盆地,在盆地内发育芒棒组碳质粉砂岩等半成岩,规划建设中的中缅公路保山-腾冲高速公路段从该套地层中通过。野外调查发现：该套碳质粉砂岩具有成岩差、易崩解、结构性强等特性,容易引发边坡失稳、隧洞围岩冒顶等工程地质问题。室内试验测试表明：该类岩石具有天然含水量高、孔隙度大、低膨胀性和有机质含量高等特点。由于有机质的胶结作用,天然岩体和干燥岩块浸水后的性状显著不同,岩体干燥后具有水稳性增强和膨胀势降低的特性。结合典型工程实例,开展了原位直接剪切试验。试验结果表明:碳质粉砂岩具有抗剪强度较高、峰残降差小等特点,是该类地层中大型不稳定斜坡体和蠕滑型滑坡发育的主要因素之一。     

Dynamic programming approach for segmentation of multivariate time series

In this paper, dynamic programming (DP) algorithm is applied to automatically segment multivariate time series. The definition and recursive formulation of segment errors of univariate time series are extended to multivariate time series, so that DP algorithm is computationally viable for multivariate time series. The order of autoregression and segmentation are simultaneously determined by Schwarz’s Bayesian information criterion. The segmentation procedure is evaluated with artificially synthesized and hydrometeorological multivariate time series. Synthetic multivariate time series are generated by threshold autoregressive model, and in real-world multivariate time series experiment we propose that besides the regression by constant, autoregression should be taken into account. The experimental studies show that the proposed algorithm performs well.