砂土液化标贯判别法在天津地区适用性分析

针对天津地区典型的饱和粉土液化判别现状以及抗震设防标准大幅调整的情况,进行标贯判别法的适用性分析。通过收集大量工程钻孔数据,结合唐山地震震后调查研究结果,对比分析国内三种规范标贯法的液化判别效果;并结合Seed简化法对明显液化区、非液化区数据进行液化回判成功率对比。研究结果表明,在Ⅶ度(0.15g)和Ⅷ度(0.20g)条件下,现行《建筑抗震设计规范》(2010版)法在天津地区液化判别的安全度最高,在液化区的判定成功率最高,但在非液化区的误判率也很高,且判定的液化程度等级与实际液化情况相比明显偏重;地标《岩土工程勘察规范》法在液化区的判定成功率较高,与现行抗规2010版接近,但对非液化区的误判率降低比较明显,判定液化程度等级与实际情况吻合性较好,既保证了安全度,又体现出了一定的经济性,对天津地区粉土液化的适用性更好。     

基于水位流量关系的流量估算不确定性分析

针对现有水位流量关系线型物理机制不强及流量估算不确定性来源考虑不充分问题,以北江流域石角水文站为例,推导该测站水位流量关系,基于BaRatin模型评估流量测量误差及率定样本选取对估算流量不确定性的影响。发现河槽控制宽浅矩形断面水位流量关系为幂函数,其系数可用糙率、河宽、比降表达,指数为定值5/3;考虑流量测量误差后高水估算流量总不确定性减小32%;率定数据增加1倍、3倍,高水估算流量总不确定性减小12%、34%。结果表明:①水位流量关系模型建立方法可推广至多类型测站;②高水测量误差对率定精度影响较大,建议提高高水流量测量精度;③现有实测水位与流量数据存在信息冗余,主要存在于低水数据中,本方法可减少率定数据使用,降低整编成本。     

基于三维稀疏反演的混合震源数据分离与一次波估计

混合震源采集（下称混采）技术是当前地震勘探的潮流。但是由混采获得的数据中包含相互重叠的由多个震源激发产生的炮记录,会对后续的地震数据处理产生严重干扰。本文针对现有的基于混采数据的稀疏反演一次波估计（EPSI）方法,提出了一种改进的基于三维稀疏反演的混采数据分离与一次波估计方法。我们将混采EPSI方法的地下一次波响应估计过程转化为基于L1范数的双凸优化问题,并用基于L1范数的谱投影梯度（SPGL1）算法进行求解,确保取得全局极值,从而稳定反演过程。此外,我们还用二维曲波变换和一维小波变换组成三维联合稀疏变换对反演过程进行约束,能在确保求解精度的同时较以往的三维曲波稀疏约束大大提高计算速度。将本文方法应用于模拟混采数据和海上实际混采数据,将试算结果与传统混采数据EPSI方法对比,全面验证了本文所述方法的有效性和优越性。     

目标函数权重对PEST-HSPF水文率定的影响研究

参数率定是水文模型构建与应用的重要基础。在多目标水文校准中,不同目标函数的权重设置直接影响校准的结果,如何确定不同目标函数的权重是水文校准的关键问题。选择典型半分布式水文模型HSPF(Hydrological Simulation Program-Fortran),基于PEST多目标校准模型,以牛栏江上游流域为研究对象,研究目标函数权重对HSPF水文模拟拟合优度的影响,为HSPF模型参数自动率定提供指导与借鉴。结果表明:(1)当PEST-HSPF水文校准的单目标函数权重(日流量、月流量、超流天数)上升时,模型的纳什系数和相对偏差呈现非线性变化特征,不规则波动幅度较大;(2)3个目标函数的权重在1~10量级内模型能够获得较高的预测能力和较低的误差,模型的纳什系数平均可达到0.8以上,相对偏差在10%以内;(3)超流量天数权重设置对模型的预测能力变化影响较大,日流量天数权重对相对偏差的波动影响较大,当超流天数的相对权重在1~1 000范围内变化时,模型纳什系数在0.02~0.86之间波动剧烈,当日流量相对权重值超过30时,相对偏差变化明显。     

底拖网渔具的采样设计对三疣梭子蟹资源量指数估算的影响

在渔业资源调查中,采样方案直接影响资源量估算的准确性,对采样方案进行优化可以提高调查数据的质量。基于2007年使用单拖网渔船在长江口——杭州湾及其邻近海域调查获得的数据,模拟出该海域三疣梭子蟹的资源数量及各季节的分布情况,作为“真值”,模拟计算不同采样方案调查获得的三疣梭子蟹资源量并与“真值”进行比较,评估不同方案的采样效果。本文利用计算机模拟了定点采样、简单随机采样、分层定点采样和分层随机采样四种采样方法,其中,定点采样和简单随机采样分别设置了9个、16个和24个采样站位,分层定点采样和分层随机采样设置了16个采样站位。三疣梭子蟹对拖网渔具的反应距离受年龄、季节和底质等条件的影响,因此,设置了1.5m,3m和5m三种反应距离。结果表明：分层采样设计优于不分层采样,简单随机采样优于定点采样。模拟结果的准确度随着采样站位数量的增加而提高,例如,定点采样方案中,9个站位时相对估算误差（REE）的最大值为163.43%,最小值为49.40%,而24个站位时REE的最大值和最小值分别是38.62%和4.15%。随着反应距离的增加,REE值和相对偏差的绝对值（RAB）逐渐降低。三疣梭子蟹的资源密集区和资源密度方差大的季节对模拟结果具有显著影响,因此,掌握该区域的资源密度情况或者进行预调查都能使采样设计方案更加合理。本研究可为底拖网渔具对三疣梭子蟹及其他物种的采样设计提供参考。     

A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39 fields for field and 56 fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjusted R² ranged from 0.20 to 0.74; jackknifed – leave-one-field-out cross validation – RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjusted R² = 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha) were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56 fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world.     

Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin,Ethiopia

Integration of remote sensing data sets from multiple satellites is tested to simulate water storage variation of Lake Ziway, Ethiopia for the period 2009-2018. Sixty Landsat ETM+/OLI images served to trace temporal variation of lake surface area using a water extraction index. Time series of lake levels were acquired from two altimetry databases that were validated by in-situ lake level measurements. Coinciding pairs of optical satellite based lake surface area and radar altimetry based lake levels were related through regression and served for simulating lake storage variation. Indices for extracting lake surface area from images showed 91–99 % overall accuracy. Lake water levels from the altimetry products well agreed to in-situ lake level measurements with R² = 0.92 and root mean square error of 11.9 cm. Based on this study we conclude that integrating satellite imagery and radar altimetry is a viable approach for frequent and accurate monitoring of lake water volume variation and for long-term change detection. Findings indicate water level reduction (4 cm/annum), surface area shrinkage (0.08km²/annum) and water storage loss (20.4Mm³/annum) of Lake Ziway (2009–2018).     

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