Algorithms for automatic,real-time tsunami detection in wind–wave measurements Part I: Implementation strategies and basic tests

Several, although not all, wind–wave gages are equipped with sensors capable of detecting sea-level oscillations within the tsunami frequency band. The present paper looks at the algorithms to be implemented in the software of these gages in order to automatically perform the real-time detection of a possible tsunami within recorded signals. In particular, a new algorithm is proposed and tested. The first part of the paper concentrates on the algorithms' characteristics, implementation strategies and basic testing. First of all, the situations in which wind–wave measurements are either essential, or useful for real-time tsunami detection are discussed. In the second place, already existing algorithms are recalled, specifying their characteristics and fields of application. Then, the characteristics of the new proposed algorithm – mainly based on an infinite impulse response, time domain filter – are illustrated and analyzed. Performance and efficiency of the considered algorithms are compared using synthetic time series. The second part of the paper discusses the algorithms' use in the framework of Tsunami Early Warning Systems (TEWS), testing them in actual cases.     

Towards an operational system for oil-spill forecast over Spanish waters: initial developments and implementation test

The ESEOO Project, launched after the Prestige crisis, has boosted operational oceanography capacities in Spain, creating new operational oceanographic services and increasing synergies between these new operational tools and already existing systems. In consequence, the present preparedness to face an oil-spill crisis is enhanced, significantly improving the operational response regarding ocean, meteorological and oil-spill monitoring and forecasting. A key aspect of this progress has been the agreement between the scientific community and the Spanish Search and Rescue Institution (SASEMAR), significantly favoured within the ESEOO framework. Important achievements of this collaboration are: (1) the design of protocols that at the crisis time provide operational state-of-the-art information, derived from both forecasting and observing systems; (2) the establishment, in case of oil-spill crisis, of a new specialized unit, named USyP, to monitor and forecast the marine oceanographic situation, providing the required met-ocean and oil-spill information for the crisis managers. The oil-spill crisis scenario simulated during the international search and rescue Exercise "Gijón-2006", organized by SASEMAR, represented an excellent opportunity to test the capabilities and the effectiveness of this USyP unit, as well as the protocols established to analyze and transfer information. The results presented in this work illustrate the effectiveness of the operational approach, and constitute an encouraging and improved base to face oil-spill crisis.     

Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired.     

建筑结构监测与抗震韧性评估

建筑结构响应是有效反映结构动力特性的最直接参数,开展结构动力响应实时监测可为结构抗震韧性评估提供准确的地震动输入。本文基于非结构构件损失构建结构抗震韧性评估方法,研究确定位移敏感型和加速度敏感型非结构构件的易损性模型。选择某六层钢筋混凝土框架结构进行实时监测系统建设,基于监测数据开展结构抗震韧性评估。通过构建建筑信息模型（BIM）,并在有限元分析软件OpenSees中建立结构弹塑性分析模型,利用实时监测数据实现结构模型更新,直至监测数据与模型分析结果一致。由于实时监测数据峰值较低,结构不会发生塑性变形,因此选择10条双向非脉冲地震动模拟实时监测地震记录。根据层间位移角和楼面加速度分布,开展结构功能损失评估,得到该建筑结构的抗震韧性得分。分析表明,该结构抗震性能较好,在遭受地震破坏后,会发生非结构构件脱落,需要采取有效措施进一步提升建筑抗震韧性水平。     

面向山地灾害动态过程仿真的地形三维建模与可视化

基于山地灾害动态过程仿真需要的考虑,并针对该动态过程仿真时渲染数据量过大所造成的实时性较差的问题,提出了利用改进的三角形二叉树LOD算法实现地形三维建模与可视化的方法。算法对山地地形数据进行了分层和分块的预处理,用三角形二叉树表示地形网格,并结合视点和局部地形的粗糙程度,动态的载入所需的地形块和释放无用的地形块,使得内存中的地形数据维持在一定范围内。实验结果表明;在对地形渲染不失真的前提下,本方法能够有效地提高地形绘制的效率,可应用到大规模山地地形的三维建模与可视化中去,为整个山地灾害的动态过程仿真奠定了良好的基础。     

河西走廊下垫面粗糙度实测值与模拟值的差异性分析

利用甘肃省河西地区17个风塔观测资料和风向风速标准差法,计算了风塔处的下垫面粗糙度,并与中尺度数值模式ARPS中使用的粗糙度参数进行了比较分析。结果表明,数值模式中使用的粗糙度值和实况具有一定差异,其使用的粗糙度定义方案并不能准确地反映出下垫面粗糙度和非均一性特征。差异最大出现在草原下垫面上,可达375%,且差异程度随下垫面植被复杂程度增加。     

沪宁高速公路无锡段春季浓雾的实时监测和若干特征

20 0 0年 2月到 4月沪宁高速公路 ,共监测到 7次低能见度 (<2 0 0m)的春雾。本文应用环境气象监测仪AMW自动气象站 ,每分钟实测的有关资料和相应的环流背景场 ,对 7次春雾形成的物理过程作了研究分析。揭示了春雾形成过程中的若干特征和设计了实施临近预报制作的思路、流程。并论述了在高速公路沿线布设AMW自动气象监测仪的基础上作出低能见度的预测是可行和可能的     

基于随机森林的PM2.5实时预报系统

针对我国当前重污染天气PM2.5浓度的实时预报问题,该文提出了一种基于随机森林算法的PM2.5浓度实时预报方法,并利用此方法对北京市地面空气质量监测数据和气象数据进行分析,建立了基于随机森林算法的PM2.5浓度实时预报模型。实验证明,该模型能够对72h内PM2.5浓度进行较高精度的实时预报,通过使用Spark分布式计算框架,能够有效降低算法耗时,文章基于此模型与Spark分布式计算框架建立了PM2.5实时预报系统。     

水力压裂微地震井地联合监测系统及仪器

水力压裂技术是油气田增产和低渗透油田开发的有效技术手段,通过监测油层裂缝延展过程中产生的微地震事件,能够有效地判断裂缝走向.现有的井中监测方法存在成本较高、施工难度大的问题;地面监测存在信号信噪比低、垂向分辨能力差等问题.本文采用井地联合微地震监测的方法,在地面布置采集仪器的同时,选择邻近的单口监测井放置井中仪器,对微地震事件进行联合监测定位.采用联合监测的方法,在水平方向和垂直方向上都能够获得较好的监测视角,对比单一监测方法提高了微地震事件的定位精度.在此方法的基础上研制的井地联合微地震监测仪器系统,解决了现有仪器系统在进行联合监测时通讯链路不通的问题,实现了在压裂施工现场对微地震联合监测数据的实时回收、处理及结果呈现.应用本系统在大庆油田进行现场监测,获取的微地震事件对压裂裂缝进行了有效评价;通过对已知射孔点进行反演定位,采用井地联合监测将定位误差由地面监测的32 m降低至14 m,验证了本文技术的有效性.     

全球Argo实时海洋观测网建设及应用进展

为了使各国政府、部门管理人员和科技工作者了解国际Argo计划的实施进展,以及帮助广大民众认识实施Argo计划的重要性和已经取得的初步成果,国际Argo信息中心、国际Argo科学组和国际Argo资料管理组联合编写了一本科普宣传册,题为“Argo,一个观测实时海洋的剖面浮标网”,经翻译、整理成文,以帮助国内读者深入了解和认识这一新世纪的重大国际观测计划。文章从陈述开展海洋观测的重要性和实施Argo计划的必要性着手,系统介绍了全球Argo实时海洋观测网建设的现状和Argo资料应用研究所取得的初步成果,以及Argo计划的发展前景等。