基于ShakeMap_CNST的2017年精河6.6级地震灾害快速评估     

刘军  谭明  宋立军  郝婧  毕雪梅  王新刚  常想德 《中国地震》2019,35(2):381-388

基于考虑区域地震动衰减关系、场地效应及震中破裂等多因素的烈度快速评估模型，结合震害预测方法，研发了一套震害预测系统，并以2017年8月9日精河M_S6.6地震的震害预测为例，将对其的预测结果与当前主流系统进行对比。结果表明，采用ShakeMap_CNST系统能对地震的影响范围和烈度进行更为准确的估计，在人员伤亡、受灾人口估计、紧急安置人数及经济损失评估等方面，相对于其他系统，本系统的结果与现场调查的结果更为接近。  相似文献   

地震活动性总体参量Rt在北京及邻区地震预测中的应用     

岳晓媛  武安绪  马梁 《地震地磁观测与研究》2019,40(6):28-34

采用地震活动性总体参量R_t方法,研究北京及邻区R_t值在中等地震前随时间的变化特征,分析跟踪地震发生前研究区域地震活动状态,探讨中等地震孕震过程的异常信息的变化特征。结果显示:当R_t值大于阈值0.84时,研究区域地震活动状态比较稳定,发生中等地震的可能性较小;反之,表明地震活动处于不稳定状态,具有发生中等地震的危险。检验R_t值的地震预测效能,预测效果比较理想,利用地震活动状态参量R_t研究北京及邻区地震活动状态,判定中等地震发生的风险性,具有一定预测意义。  相似文献   

Effects of wave directionality on extreme response for a long end-anchored floating bridge     

《Applied Ocean Research》2019

Reliable design codes are of great importance when constructing new civil engineering concepts such as floating bridges. Previously only a scarce number of floating bridges have been built in rough wave conditions and only limited knowledge of the extreme environmental conditions and the associated extreme response exists. To form a better design basis an increased understanding of the sensitivity in the structural response towards changes in short-crested sea parameters is needed. Furthermore, acquiring the necessary accuracy in simulated extreme response is often a computationally expensive endeavour and the number of simulations needed is often based on experience. The present study investigates the wave-induced short-term extreme response of a simplified end-anchored floating bridge concept for several wave environments with a return period of 100 years. The study includes convergence of the coefficient of variation for the extreme response for different realization lengths as well as number of realizations. The sensitivity in the structural response towards different main wave directions and spreading exponents is investigated and includes both transverse and vertical displacement response spectra and extreme Von Mises stress in the bridge girder cross-section. The extreme response is based on an accuracy of 2% in the coefficient of variation equivalent to 40 3-h realizations and a low sensitivity in the response is found for natural occurring spreading exponents and for main wave directions within 15° from beam sea.  相似文献   

FluBiDi – a model to estimate flood based on runoff: validation using extreme and natural basin conditions     

Faustino De Luna-Cruz  O.A. Fuentes-Mariles  J. Gracia-Sánchez 《水文科学杂志》2019,64(3):297-317

FluBiDi is a two-dimensional model created to simulate real events that can take days and months, as well as short events (minutes or hours) and inclusive laboratory tests. To verify the robustness of FluBiDi, it was tested using a previous study with both designed and real digital elevation models. The results highlight good agreement between the models (i.e. Mike Flood, SOBEK, ISIS 2D, and others) tested and FluBiDi (around 90% for a specific instant and 95% for the complete time simulation). In the simulated hydrographs, the discharge peak value, time to peak, and water level results were accurate, reproducing them with an error of less than 5%. The velocity differences observed in a couple of tests in FluBiDi were associated with very short periods of time (seconds). However, FluBiDi is highly accurate for simulating floods under real topographical conditions with differences of around 2 cm when water depth is around 150 cm. The average water depth and velocities are precise, and the model describes with high accuracy the pattern and extent of floods. FluBiDi has the capability to be adjusted to different types of events and only requires limited input data.  相似文献   

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India     

Mohd Yawar Ali Khan  Fuqiang Tian  Faisal Hasan  Govind Joseph Chakrapani 《国际泥沙研究》2019,34(2):95-107

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008–2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R²), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.  相似文献   

新一代区域海-气-浪耦合台风预报系统   总被引：1，自引：0，他引：1  

雷小途  李永平  于润玲  李泓  汤杰  段自强  郑运霞  方平治  赵兵科  曾智华  黄伟  鲍旭炜  喻自凤  陈国民  马雷鸣  骆婧瑶  张帅  林立旻 《海洋学报》2019,41(6):123-134

依托国家重点基础研究（973）计划项目"上层海洋对台风的响应和调制机理研究"，中国气象局上海台风研究所联合国家海洋局的相关单位，通过实施近海台风的外场观测科学试验、加强台风边界层（特别是海气相互作用）物理过程诊断分析及参数化方案等的研究，建立并改进了台风强度预报的海-气-浪耦合预报模式系统，并在此基础上发展了台风强度的集合预报技术，在历史典型台风个例和2016-2017年台汛期的业务化测试中表现出良好的预报性能。  相似文献   

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach     

Jian Zhao  Yanguo Fan  Yuxiang Mu 《Marine Geodesy》2019,42(4):344-366

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.  相似文献   

Estimation of daily stream water temperatures with a Bayesian regression approach          下载免费PDF全文

Mohammad M. Sohrabi  Rohan Benjankar  Daniele Tonina  Seth J. Wenger  Daniel J. Isaak 《水文研究》2017,31(9):1719-1733

Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data.  相似文献   

Basin‐scale methods for predicting salmonid spawning habitat via grain size and riffle spacing,tested in a California coastal drainage          下载免费PDF全文

Allison M. Pfeiffer  Noah J. Finnegan 《地球表面变化过程与地形》2017,42(6):941-955

Basin‐scale predictive geomorphic models for river characteristics, particularly grain size, can aid in salmonid habitat identification. However, these basin‐scale methods are largely untested with actual habitat usage data. Here, we develop and test an approach for predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)‐derived topographic data for a 77 km² watershed along the central California Coast. This approach improves on previous efforts in that it predicts the full grain size distribution and incorporates an empirically calibrated shear stress partitioning factor. The predicted grain size distributions are used to calculate the fraction of the bed area movable by spawning fish. We then compare the ‘movable fraction’ with 7 years of observed spawning data. We find that predicted movable fraction explains the paucity of spawning in the upper reaches of the study drainage, but does not explain variation along the mainstem. In search of another morphologic characteristic that may help explain the variation within the mainstem, we measure riffle density, a proxy for physical habitat complexity. We find that field surveys of riffle density explain 64% of the variation in spawning in these mainstem reaches, suggesting that within reaches of appropriate sized gravel, spawning density is related to riffle density. Because riffle density varies systematically with channel width, predicting riffle spacing is straightforward with LiDAR data. Taken together, these findings demonstrate the efficacy of basin‐scale spawning habitat predictions made using high‐resolution digital elevation models. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

改进的AVAZ方法在裂缝型储层预测中的应用研究          下载免费PDF全文

舒梦珵 《CT理论与应用研究》2017,26(1):45-52

对于裂缝型油气藏，基于叠前方位角地震数据体，利用AVAZ方法进行裂缝属性预测是目前较为有效的裂缝描述手段，然而，由于地震数据通常为窄方位采集，使得不同方位角数据覆盖次数差异较大，导致各方位振幅能量分布不均，最终造成裂缝属性预测中产生趋势性误差。振幅归一化处理技术应用到现有的AVAZ方法中，该方法能够有效地降低因数据采集所造成的趋势性误差，预测结果会更为真实、可靠。在实际油田的应用中，振幅归一化技术显著改善了AVAZ方法对裂缝属性预测的准确性，取得了较好的效果。   相似文献