抗差估计的多波束测深数据内插方法

针对传统多波束测深数据网格化内插方法抗差性不足的问题,该文将抗差估计理论应用于网格化内插方法中。基于不确定度指标建立了距离不确定度联合加权内插模型,在该模型的权函数中引入了等价权,同时给出了等价权设计方案及迭代初值确定原则,以增强模型的抗差性;介绍了一种节点邻域参考点合理选取方法,给出了改进内插法实现的基本步骤,最后通过实测数据对反距离加权法、联合加权法及改进方法内插水深质量进行比较。结果表明,改进方法内插结果要优于另外两种方法,可应用于多波束测深数据网格化处理中。     

Dasymetric Spatiotemporal Interpolation

This research applies the principles of dasymetric mapping to spatiotemporal interpolation by extending the spatial concepts of zone and area to their temporal analogs of interval and duration, respectively. An example application of dasymetric spatiotemporal interpolation using crime event data is presented. Results indicate that dasymetric spatiotemporal interpolation significantly improves the accuracy of estimates over areal or duration weighting. In addition, even when dasymetric interpolation in either the spatial or temporal dimension is relatively weak, combining dasymetric estimation in both space and time dimensions simultaneously has the potential to amplify the accuracy of the overall dasymetric estimation.     

Hybrid N-order Lagrangian Interpolation Eulerian?Lagrangian Method for Salinity Calculation

The Eulerian?Lagrangian method (ELM) has been used by many ocean models as the solution of the advection equation, but the numerical error caused by interpolation imposes restriction on its accuracy. In the present study, hybrid N-order Lagrangian interpolation ELM (LiELM) is put forward in which the N-order Lagrangian interpolation is used at first, then the lower order Lagrangian interpolation is applied in the points where the interpolation results are abnormally higher or lower. The calculation results of a step-shaped salinity advection model are analyzed, which show that higher order (N=3?8) LiELM can reduce the mean numerical error of salinity calculation, but the numerical oscillation error is still significant. Even number order LiELM makes larger numerical oscillation error than its adjacent odd number order LiELM. Hybrid N-order LiELM can remove numerical oscillation, and it significantly reduces the mean numerical error when N is even and the current is in fixed direction, while it makes less effect on mean numerical error when N is odd or the current direction changes periodically. Hybrid odd number order LiELM makes less mean numerical error than its adjacent even number order LiELM when the current is in the fixed direction, while the mean numerical error decreases as N increases when the current direction changes periodically, so odd number of N may be better for application. Among various types of Hybrid N-order LiELM, the scheme reducing N-order directly to 1st-order may be the optimal for synthetic selection of accuracy and computational efficiency.     

基于非均质地层参数表征的颗粒流模型构建

非均质地层参数的获取与表征方法是浅层地质体改造的重要依据。基于非均质地层的地质建模,表征具有随机不确定性和模糊不确定性的土体表观参数和物理力学参数。利用 Weibull 分布统计描述局部地层信息,并应用Diamond-Square分形插值方法,结合差分盒子维计算非均质地层分形维数,进一步合理演算整体地层参数。采用PFC3D数值模拟软件,结合马尔科夫链蒙特卡洛方法采样数据,对非均质地层块石颗粒及土颗粒进行建模。通过非均质地层模拟结果,发现其破坏过程与应力应变曲线要比均质土体复杂得多,由于块石大颗粒的含量、分布特性对土样力学性质影响显著。本文建模方法有助于非均质地层参数表征研究。     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

THE SETTLEMENT OF A RIGID CIRCULAR FOUNDATION RESTING ON A HALF-SPACE EXHIBITING A NEAR SURFACE ELASTIC NON-HOMOGENEITY

The present paper examines the elastostatic problem pertaining to the axisymmetric loading of a rigid circular foundation resting on the surface of a non-homogeneous elastic half-space. The non-homogeneity corresponds to a depth variation in the linear elastic shear modulus according to the exponential form G(z)=G₁+G₂e^-ζz. The equations of elasticity governing this type of non-homogeneity are solved by employing a Hankel transform technique. The mixed boundary value problem associated with the indentation of the half-space by the rigid circular foundation is reduced to a Fredholm integral equation which is solved via a numerical technique. The numerical results presented in the paper illustrate the influence of the near-surface elastic non-homogeneity on the settlement of the foundation.     

平缓地区地形湿度指数的计算方法

地形湿度指数( topographic wetness index) 可定量模拟流域内土壤水分的干湿状况, 在流域 的土壤及分布式水文模型等研究中具有重要的意义。但现有的地形湿度指数计算方法在应用于 地形平缓地区时会得到明显不合理的结果, 即在河谷地区内, 地形湿度指数仅在狭窄的汇水线上 数值较高, 而在汇水线以外的位置则阶跃式地变为异常低的地形湿度指数值。本文针对此问题对 地形湿度指数的计算方法提出改进: 以多流向算法MFD- fg 计算汇水面积, 相应地以最大下坡计 算地形湿度指数, 再基于一个正态分布函数对河谷平原地区内的地形湿度指数进行插值处理。应 用结果表明, 所得地形湿度指数的空间分布不但能合理地反映平缓地区坡面上的水分分布状况, 并且在河谷地区内地形湿度指数值也都比较高, 其空间分布呈平滑过渡, 因而整个研究区域的水 分分布状况得到了比较合理的反映。     

长江口滨岸湿地无机氮界面交换通量量算

基于3年长江口滨岸湿地沉积物－水界面无机氮季节性交换通量连续实测数据,建立无机氮界面交换通量空间插值模型与量算模型,对无机氮界面交换通量季节性空间分布特征、滨岸湿地不同岸段无机氮季节性界面交换总通量量算等研究。结果表明：修正GIDS插值模型在无机氮界面交换通量空间插值预测过程中精度明显优于IDS方法,而略优于普通Kriging方法;长江口滨岸湿地沉积物－水界面无机氮交换通量空间分布在不同季节表现出复杂的空间分异特征;利用修正GIDS插值模型对长江口滨岸湿地无机氮交换通量进行空间插值过程中,为提高通量量算模型精度,应采用1.2'×1.2'的空间尺度为最佳;长江口滨岸湿地无机氮界面交换总通量量算表明,长江口滨岸湿地在春季向水体释放无机氮,是水体无机氮的释放源,释放量为1.33×10⁴ t,夏季、秋季和冬季表现为净化水体中无机氮,是水体无机氮的吸收汇,分别净化无机氮量为4.36×10⁴ t、6.81×10⁴ t和2.24×10⁴ t,全年总体表现为净化水体中无机氮,净化量为12.1×10⁴ t;长江口多年水体中无机氮通量多项式拟合分析得出,2002~2004年3年长江口水体中无机氮通量平均值为52.6×10⁴ t,滨岸湿地对长江口水体中无机氮的年均净化率达23.0%。     

基于GPU加速的Boussinesq类波浪传播变形数值模型

Boussinesq波浪模型是一类相位解析模型,在时域内求解需要较高的空间和时间分辨率以保证计算精度。为提高计算效率,有必要针对该类模型开展并行算法的研究。与传统的中央处理器(CPU)相比,图形处理器(GPU)有大量的运算器,可显著提高计算效率。基于统一计算设备架构CUDA C语言和图形处理器,实现了Boussinesq模型的并行运算。将本模型的计算结果同CPU数值模拟结果和解析解相比较,发现得到的结果基本一致。同时也比较了CPU端与GPU端的计算效率,结果表明,GPU数值模型的计算效率有明显提升,并且伴随数值网格的增多,提升效果更为明显。     

利用水准数据的天津市地面沉降时空特征分析

地面不均匀沉降可能对城市的发展与人民的安全造成危害,天津市的地面沉降情况尤为严重。针对该问题,本文收集天津市2005—2012年、2016—2017年水准观测数据,以固定水准点位的沉降量、沉降速率、沉降加速率为状态向量,构建卡尔曼滤波模型,对天津市历年的水准观测数据进行滤波;根据滤波后的结果,本文利用多项式加权内插的方法,以距离、沉降速率、沉降加速率信息确定权值大小,对地面沉降情况进行内插;并以中误差作为精度评定参数,比较多种内插方法的精度。通过对内插结果的试验分析发现,2005—2017年天津市地面累计平均沉降量为394.477 5 mm,最大沉降量为1 143.5 mm;主要沉降区域为北辰、大港、塘沽等地区,且随着时间的增长,这些区域呈现漏斗式下沉。试验证明本文结合卡尔曼滤波与多项式加权内插的方式能够较好地反映地面沉降的时空特征分布情况并对未来一段时间的沉降情况进行预测,对天津市的城市发展及建设有一定的参考意义。