Bias estimation and correction for triangle-based surface area calculations

The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data.     

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.     

矿业权边界在资源储量估算垂直纵投影图上的确定

利用垂直纵投影图进行资源储量估算时,当矿体走向与矿业权边界斜交时,矿业权边界在资源储量垂直纵投影图上投影的准确位置较难确定。该文介绍了利用不同中段平面图和利用计算公式确定矿业权边界在资源储量垂直纵投影图上的投影位置,对解决此类问题具有一定的参考价值。     

Application of Linear Mean-Square Estimation in Ocean Engineering

The attempt to obtain long-term observed data around some sea areas we concern is usually very hard or even impossible in practical offshore and ocean engineering situations. In this paper, by means of linear mean-square estimation method, a new way to extend short-term data to long-term ones is developed. The long-term data about concerning sea areas can be constructed via a series of long-term data obtained from neighbor oceanographic stations, through relevance analysis of different data series. It is effective to cover the insufficiency of time series prediction method’s overdependence upon the length of data series, as well as the limitation of variable numbers adopted in multiple linear regression model. The storm surge data collected from three oceanographic stations located in Shandong Peninsula are taken as examples to analyze the number-selection effect of reference oceanographic stations (adjacent to the concerning sea area) and the correlation coefficients between sea sites which are selected for reference and for engineering projects construction respectively. By comparing the N-year return-period values which are calculated from observed raw data and processed data which are extended from finite data series by means of the linear mean-square estimation method, one can draw a conclusion that this method can give considerably good estimation in practical ocean engineering, in spite of different extreme value distributions about raw and processed data.     

Investigation of gas content of organic-rich shale:A case study from Lower Permian shale in southern North China Basin,central China

Measuring gas content is an essential step in estimating the commerciality of gas reserves. In this study,eight shale core samples from the Mouye-1 well were measured using a homemade patented gas desorption apparatus to determine their gas contents. Due to the air contamination that is introduced into the desorption canister, a mathematical method was devised to correct the gas quantity and quality.Compared to the chemical compositions of desorbed gas, the chemical compositions of residual gas are somewhat different. In residual gas, carbon dioxide and nitrogen record a slight increase, and propane is first observed. This phenomenon may be related to the exposure time during the transportation of shale samples from the drilling site to the laboratory, as well as the differences in the mass, size and adsorptivity of different gas molecules. In addition to a series of conventional methods, including the USBM direct method and the Amoco Curve Fit(ACF) method, which were used here for lost gas content estimation, a Modified Curve Fit(MCF) method, based on the 'bidisperse' diffusion model, was established to estimate lost gas content. By fitting the ACF and MCF models to gas desorption data, we determined that the MCF method could reasonably describe the gas desorption data over the entire time period, whereas the ACF method failed. The failure of the ACF method to describe the gas desorption process may be related to its restrictive assumption of a single pore size within shale samples. In comparison to the indirect method, this study demonstrates that none of the three methods studied in this investigation(USBM, ACF and MCF) could individually estimate the lost gas contents of all shale samples and that the proportion of free gas relative to total gas has a significant effect on the estimation accuracy of the selected method. When the ratio of free gas to total gas is lower than 45%, the USBM method is the best for estimating the lost gas content, whereas when the ratio ranges from 45% to 75% or is more than 75%, the ACF and MCF methods, are the best options respectively.     

融雪洪水跳跃变异对其参数估计不确定性影响分析

利用Pettitt非参数检验法和Mann-Kendall非参数趋势检验法,分析年最大洪峰流量序列的非一致性,确定序列的变异形式,采用“分解-合成”理论对其进行一致性修正,得到过去、现状两种条件下年最大洪峰流量序列,根据贝叶斯理论对序列一致性修正前后参数不确定性进行估计,并对其预报区间优良性进行评价。研究结果表明：年最大洪峰流量序列变异点发生在1993年,序列整体上升趋势不显著,在1957-1993年子序列呈显著下降趋势,而1994-2006年子序列变化趋势不显著,跳跃变异为序列主要变异形式;给出了实测、还原及还现序列参数后验分布估计值及95%置信区间,将其结合优化适线法进行P-Ⅲ型频率分析,得到修正前后设计频率年最大洪峰流量预报区间估计值;还原、还现序列与实测序列相比,预报区间覆盖率均提高24%,平均带宽分别减少39.59%、23.17%,平均偏移幅度分别减少28.45%、11.39%。通过对非一致性年最大洪峰流量序列还原/还现计算,可减小参数估计不确定性对其计算产生的影响,从而提高预报区间的可靠性。     

Satellite estimates and subpixel variability of rainfall in a semi-arid grassland

卫星估雨精度的不确定性受到当地降雨类型和像元内降雨非均匀性影响,而结合这两个关键因素开展半干旱草原卫星估雨的研究有限.2009年夏,我们在中国锡林郭勒半干旱草原用多部微雨雷达和雨量计构建了9 km卫星像元降雨观测网,观测了像元内降雨非均匀性(空间变异系数CV),并评估了卫星估雨精度.结果表明:(1)CV值受像元内平均降雨量,降雨类型,降雨云面积及移向等影响,如高Cv值的降雨过程大多为平均降雨量小,对流性降雨过程,降雨云边缘像元CV值较高;(2)TRMM 3B42V7卫星估雨产品适用性较好,CMORPH和PERSIANN次之,但TRMM 3B42V7易在半干旱草原湖泊处高估降雨.     

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.     

花岗岩风化料弃土快速堆填过程中不排水抗剪强度评估

沿海沿江城市地下开挖产生的工程渣土含泥量大、含水率高且较松散,主要运往渣土场进行堆填处置。由于产量巨大而处置场地有限,许多渣土场在运营过程中存在堆填速度快、缺乏排水设施、超高超库容堆填等问题,容易引发堆填体失稳事故。目前对于非饱和工程渣土堆体在快速堆填过程中的失稳机制认识尚不清晰,尤其是对这一过程中的高饱和度工程渣土强度增长规律缺乏足够的认识。以深圳红坳渣土场填料——花岗岩风化料(CDG)填土为研究对象,对不同初始饱和度土样进行三轴不排水不排气等向压缩和剪切试验,结果表明:非饱和CDG填土不排水抗剪强度随围压增大呈非线性增长,增长速率与试样初始饱和度密切相关;当土样压缩后的饱和度超过0.7,不排水强度随围压的增长速率显著降低。基于有效固结应力法的原理,结合Hilf孔压公式和修正剑桥模型,提出了一种工程渣土不排水抗剪强度估算方法,并通过与试验结果对比验证了该方法对初始饱和度高于0.6的CDG填土的适用性。利用该方法确定的不排水强度cu与正应力σn的关系可应用于高饱和度工程渣土快速堆填中的稳定性分析。     

基于优化经验模态分解和聚类分析的滑坡位移智能预测研究

针对三峡库区"阶跃式"滑坡的变形特征,提出了一种新的滑坡位移预测方法。以白水河滑坡ZG118和XD-01监测点位移数据为例,采用基于软筛分停止准则的经验模态分解(SSSC-EMD)将累计位移-时间曲线和影响因子时间序列自适应地分解为多个固有模态函数(IMF),并采用K均值(K-Means)聚类法对其进行聚类累加,得到有物理含义的位移分量(趋势性位移、周期性位移以及随机性位移)和影响因子分量(高频影响因子和低频影响因子)。使用最小二乘法对趋势性位移进行拟合预测;采用果蝇优化-最小二乘支持向量机(FOA-LSSVM)模型对周期性位移和随机性位移进行预测。将各位移分量预测值进行叠加处理,实现滑坡累计位移的预测。研究结果表明,所提出的(SSSC-EMD)-K-Means-(FOA-LSSVM)模型能够预测"阶跃式"滑坡的位移变化规律,且预测精度高于传统的支持向量机回归(SVR)、最小二乘支持向量机(LSSVM)模型;并通过改变训练集长度,进行单因素分析,发现其与预测精度之间呈正相关关系。