DATA ANALYSIS IN THE SHOT NOISE LIMIT.PART Ⅲ:ADAPTIVE DATA SMOOTHING FILTERS

Digital filter smoothing methods for shot-noise-limited data are addressed in this study.The preferredmethod is based on a Gaussian filter in which the width of the Gaussian filter function is varied dependingon the estimate of the second derivative of the raw data.This filter is developed from the standpoint ofmaximum likelihood parameter estimation of the probability density function which describes shot-noise-limited data.The smoothing filter is tested and compared with the conventional sequential regressionfilter.This adaptive Gaussian smoothing filter works better than both the sequential regression and theadaptive Gaussian filter derived for normal noise.For data containing both high-and low-frequencycomponents,the limiting step in the adaptive filter is an estimation of the smoothing interval.Methodsfor determining an optimum smoothing interval are discussed.With the optimized smoothing interval,the adaptive Gaussian filter works well for data sets with a wide range of varying frequency components.In particular,synthetic data typical of atomic emission spectra are used to test this smoothing filter.     

Resolution analysis for discrete systems

summary . Treatments of geophysical inverse problems have tended to polarize into approaches intended to generate models either described by piecewise continuous functions or with some prior discretization. The two approaches are here developed in parallel, and the ideas of a trade-off between the anticipated error and the attainable level of detail in the model estimate are extended to the discrete case, either with even or uneven discretization.
An alternative approach to specifying the potential resolution of a model is to establish upper and lower bounds on parameter values. Linear programming methods are extended to determine bounds which allow for subjective limits on parameter values.
For a non-linear system the possible resolution may be investigated by estimation procedures based on the full set of successful solutions obtained by Monte-Carlo inversion.     

加速遗传算法在马斯京根洪水演算模型参数估计中的应用

为提高马斯京根洪水演算模型参数估计的准确性、稳定性和工作效率,根据马斯京根洪水演算模型的基本假定把模型参数估计问题转换为相应的优化问题,并提出用加速遗传算法(AGA)同时优化模型参数。实例计算的结果说明了用AGA进行参数估计的有效性和较高的演算精度,实现了参数估计的优化和简化,在洪水灾害管理中具有推广应用价值。     

Biomass equations for shrub species of Tamaulipan thornscrub of North-eastern Mexico

Nine additive allometric equations for computing above-ground, standing biomass were developed for the plant community and for each of 18 single species typical of the Tamaulipan thornscrub of north-eastern Mexico. Equations developed using additive procedures in seemingly unrelated linear regression provided statistical efficiency in total biomass estimates at the scales of both individual species and at the plant community. A single equation for each species improves efficiency in biomass estimates by 12.5% in contrast to using a single equation for the plant community. Therefore, additive equations developed in seemingly unrelated linear regression of parameter estimation are recommended to compute biomass components and total biomass for the species described.     

Approximation of Gaussian spatial autoregressive models for massive regular square tessellation data

Most of the literature to date proposes approximations to the determinant of a positive definite n × n spatial covariance matrix (the Jacobian term) for Gaussian spatial autoregressive models that fail to support the analysis of massive georeferenced data sets. This paper briefly surveys this literature, recalls and refines much simpler Jacobian approximations, presents selected eigenvalue estimation techniques, summarizes validation results (for estimated eigenvalues, Jacobian approximations, and estimation of a spatial autocorrelation parameter), and illustrates the estimation of the spatial autocorrelation parameter in a spatial autoregressive model specification for cases as large as n = 37,214,101. The principal contribution of this paper is to the implementation of spatial autoregressive model specifications for any size of georeferenced data set. Its specific additions to the literature include (1) new, more efficient estimation algorithms; (2) an approximation of the Jacobian term for remotely sensed data forming incomplete rectangular regions; (3) issues of inference; and (4) timing results.     

Non-linear stochastic models for seismic events with applications in event identification

Summary. A special class of p th-order autoregressive stochastic models is proposed for seismic records of explosions and earthquakes. These models are based on deterministic formulations proposed by Haskell, Von Seggern & Blandford and Aki. Statistical properties of the estimation procedure are derived and the model applied to seismic records of P waves. The method is shown to have applications in depth and reflection parameter estimation and in event discrimination.     

水文模型参数敏感性分析—优化—区域化方法研究进展

水文模型是认识水文科学规律、分析水文过程及研究水文循环机理的重要科学工具。水文模型模拟结果的不确定分析是提高模型可靠性、进行有效水情预报的一个重要研究内容。参数不确定性是影响水文模型模拟结果不确定性的关键因素之一,开展模型参数不确定性及其影响因素分析对水文预报具有重要现实意义。目前的参数不确定性分析方法大致可分为3类：参数敏感性分析、参数优化以及考虑无资料流域参数值估计的参数区域化方法。论文归纳总结了近年来国内外水文模型参数不确定性分析工作的主要研究进展,分析了不同方法的优点与不足,提出了未来水文模型不确定性分析方法研究的潜在发展方向。借助多学科理论和技术方法,加强水文模型不确定性分析系统性方法的研究,是水文学科当前的迫切需求及发展趋势。     

A method to obtain soil-moisture estimates over bare agricultural fields in arid areas by using multi-angle RADARSAT-2 data

Soil moisture is an important parameter for agriculture, meteorological, and hydrological studies. This paper focuses on soil-moisture estimation methodology based on the multi-angle high-and low-incidence-angle mode RADARSAT-2 data obtained over bare agricultural fields in an arid area. Backscattering of the high-and low-incidence angles is simulated by using AIEM(advanced integral equation model), with the surface-roughness estimation model built based on the simulated data. Combining the surface-roughness estimation model with the backscattering model of the low-incidence-angle mode, a soil-moisture estimation method is put forward. First, the natural logarithm(ln) of soil moisture was obtained and then the soil moisture calculated. Soil moisture of the study area in Dunhuang, Gansu Province, was obtained based on this method; a good agreement was observed between the estimated and measured soil moisture. The coefficient of determination was 0.85, and the estimation precision reached 4.02% in root mean square error(RMSE). The results illustrate the high potential of the approach developed and RADARSAT-2 data to monitor soil moisture.     

Application of geodetector in sensitivity analysis of reference crop evapotranspiration spatial changes in Northwest China

Reference crop evapotranspiration (ET_0) is an important parameter in the research of farmland irrigation management,crop water demand estimation and water balance in scarce data areas,therefore,it is very important to study the factors af‐fecting the spatial variation of ET_0.In this paper,the Penman-Monteith formula was used to calculate ET_0which is the de‐pendent variable of elevation (Elev),daily maximum temperature (T_(max)),daily minimum temperature (T_(min)),daily average temperature (T_(mean)),wind speed (U_2),sunshine duration (SD) and relative humidity (RH).The sensitivity analysis of ET_0was performed using a Geodetector method based on spatial stratified heterogeneity.The applicability of Geodetector in sensitivity analysis of ET_0was verified by comparing it with existing research results.Results show that RH,T_(max),SD,and T_(mean)are the main factors affecting ET_0in Northwest China,and RH has the best explanatory power for the spatial distribu‐tion of ET_0.Geodetector has a unique advantage in sensitivity analysis,because it can analyze the synergistic effect of two factors on the change of ET_0.The interactive detector of Geodetector revealed that the synergistic effect of RH and T_(mean)on ET_0is very significant,and can explain 89%of the spatial variation of ET_0.This research provides a new method for sensitivity analysis of ET_0changes.     

集成遥感数据的陆地净初级生产力估算模型研究综述

净初级生产力(NPP)是衡量碳循环、指导土地利用、评估生态安全、指示环境变化、反映粮食安全等的重要参量,其估算受模型构建机理和生态系统关键地表参数输入的影响。近年来,随着遥感数据的不断丰富和遥感处理技术的快速发展,集成遥感数据的NPP估算模型相较于仅采用气候、土壤等传统观测数据的非遥感模型,在分析时空异质性等方面的优势日益凸显。本文基于Web of Science和CNKI两大数据库,采用文献统计分析方法,系统回顾NPP研究概况及国内外集成遥感数据的NPP估算模型的近期进展;并将集成遥感数据进行NPP估算的模型分为统计模型、光能利用率模型、过程模型及耦合模型四类;重点阐述了各类遥感估算模型的机理、差异性、适宜性及局限性;最后,在分析NPP遥感估算面临困境和科学挑战的基础上,从机理与影响因素、数据基础、参数反演、时空尺度拓展、软硬件支撑等方面对未来研究进行了展望。     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

中国省域犯罪率影响因素的空间非平稳性分析

收入差距和流动人口是影响犯罪率的两个重要因素, 以往研究基于OLS模型, 在假设地域空间为均质的前提下分析其对犯罪率的影响, 但现实世界的空间单元往往难以满足“均质”的假设, 多数表现为“空间异质”。以OLS计量空间异质会造成计量结果出现偏差, 同时无法了解不同空间单元的不同影响。而地理加权回归模型通过将空间结构嵌入线性回归模型中, 很好的解决了空间异质的计量问题。利用地理加权回归模型研究2008 年中国大陆省域单元犯罪率的影响因素, 结果表明:① 犯罪率的影响因素表现出空间非平稳性, 流动人口与犯罪率显著相关, 但各个省份相关程度并不相同, 影响关系随空间位置变化而变化;② 地理加权回归模型的计量精度和拟合度比OLS模型有大幅提高     

光合有效辐射（PAR）估算的研究进展

光合有效辐射（Photo-synthetically Active Radiation,PAR）是研究全球变化与陆地生态系统变化的核心之一,不仅是衡量生态系统光合作用变化、碳收支变化的重要数据来源,也是反映全球气候变化主要驱动因子。本文在回顾PAR估算方法的基础上,综合分析了传统方法中的气候学方法、模型参数方法,及在此基础上发展起来的基于遥感的转换系数、模型化参数法的优缺点,并对PAR估算方法研究做出了展望。气候学法、模型参数法等传统方法一直是进行PAR估算的常用手段。然而,随着对生态系统研究的日益深入,基于遥感的估算方法已经成为一种新的手段,凭借遥感覆盖范围广的优势,它使得获取区域乃至全球PAR估算成为可能。因此,如何利用遥感数据获取长时间序列PAR,成为目前关注的重点之一。已有研究表明,查找表方法之类的定量化估算方法将成为主要的估算方法,它不仅在机理上解释了PAR的传输过程,而且增强了估算方法的可靠性、可操作性与普适性。     

三江源植被净初级生产力估算研究进展（英文）

The Three-River Headwater Region(TRHR), known as the "Water Tower of China", is an important ecological shelter for national security interests and regional sustainable development activities for many downstream regions in China and a number of Southeast Asian countries. The TRHR is a high-elevation, cold environment with a unique, but typical alpine vegetation system. Net primary productivity(NPP) is a key vegetation parameter and ecological indicator that can reflect both natural environmental changes and carbon budget levels. Given the unique geographical environment and strategic location of the TRHR, many scholars have estimated NPP of the TRHR by using different methods; however, these estimates vary greatly for a number of reasons. To date, there is no paper that has reviewed and assessed NPP estimation studies conducted in the TRHR. Therefore, in this paper, we(1) summarized the related methods and results of NPP estimation in the TRHR in a systematic review of previous research;(2) discussed the suitability of existing methods for estimating NPP in the TRHR and highlighted the most significant challenges; and(3) assessed the estimated NPP results. Finally, developmental directions of NPP estimation in the TRHR were prospected.     

A systematic review of research studies on the estimation of net primary productivity in the Three-River Headwater Region,China

The Three-River Headwater Region (TRHR), known as the “Water Tower of China”, is an important ecological shelter for national security interests and regional sustainable development activities for many downstream regions in China and a number of Southeast Asian countries. The TRHR is a high-elevation, cold environment with a unique, but typical alpine vegetation system. Net primary productivity (NPP) is a key vegetation parameter and ecological indicator that can reflect both natural environmental changes and carbon budget levels. Given the unique geographical environment and strategic location of the TRHR, many scholars have estimated NPP of the TRHR by using different methods; however, these estimates vary greatly for a number of reasons. To date, there is no paper that has reviewed and assessed NPP estimation studies conducted in the TRHR. Therefore, in this paper, we (1) summarized the related methods and results of NPP estimation in the TRHR in a systematic review of previous research; (2) discussed the suitability of existing methods for estimating NPP in the TRHR and highlighted the most significant challenges; and (3) assessed the estimated NPP results. Finally, developmental directions of NPP estimation in the TRHR were prospected.     

基于空间计量模型的城市网络外部性定量测度

在经济全球化、区域一体化的背景下,城市发展动力由内部功能集聚转向外部关系协调,城市间的关联作用愈发重要,成为“城市的第二本质”。通过文献计量分析,发现空间计量模型逐渐成为城市网络外部性定量测度的主要方法之一。从研究设计角度,空间计量模型作为城市网络外部性的定量建模工具,存在至少两方面的问题：城市网络外部性的非对称效应和城市网络的多尺度问题。本文结合城市网络外部性测度与建模,首先阐述如何规范地解读空间计量模型参数及其与网络外部性测度的对应关系;其次基于蒙特卡洛模拟实验论证非对称城市网络外部性和多尺度城市网络外部性的建模方法。研究表明：(1)忽视城市网络外部性中潜在的非对称效应会显著降低模型参数估计的准确性,本文开发的非对称空间效应模型（Asymmetric Spatial Econometric Model,ASEM）可以准确识别网络外部性的非对称效应,给出更加准确的参数估计;(2)忽略现实存在的多尺度网络效应,只在单一尺度对网络效应建模会造成参数估计失真,而空间多尺度统计模型（Hierarchical Spatial Autoregressive model,HSAR）为多尺度网络外部...     

Estimation of the pole tide gravimetric factor at the chandler period through wavelet filtering

Wavelet analysis for filtering is used to improve estimation of gravity variations induced by Chandler wobble. This method eliminate noise in superconducting gravimeter (SG) records with bandpass filters derived from Daubechies wavelet. The SG records at four European stations (Brussels, Membach, Strasbourg and Vienna) are analysed in this study. First, the earth tidal constituents are removed from the observed data by using synthetic tides, then the gravity residuals are filtered into a narrow period band of 256–512 d by a wavelet bandpass filter. These data are submitted to three regression analysis methods for estimating the gravimetric factor of the Chandler wobble. After processing by wavelet filtering, SG records can provide amplitude factors δ and phase lags κ of the Chandler wobble with much smaller mean square deviation (MSD) than these provided by former studies. It is mainly because the wavelet method can effectively eliminate instrumental drift and provide smoothed data series for the regression analysis.     

A case study of model selection and parameter inference by maximum likelihood with application to uncertainty analysis

One of the uses of geostatistical conditional simulation is as a tool in assessing the spatial uncertainty of inputs to the Monte Carlo method of system uncertainty analysis. Because the number of experimental data in practical applications is limited, the geostatistical parameters used in the simulation are themselves uncertain. The inference of these parameters by maximum likelihood allows for an easy assessment of this estimation uncertainty which, in turn, may be included in the conditional simulation procedure. A case study based on transmissivity data is presented to show the methodology whereby both model selection and parameter inference are solved by maximum likelihood.     

Technological risk regulation of the subgrade base stability in permafrost areas

Maintenance and risk estimation of a weak roadbed condition during the construction period are described.Risk-management procedure for unstable,weak soils is proposed through adjusting the technological loads and real-time monitoring of their influence.The efficiency of implementing maximum allowable vibroloads and the drainage system for strengthening weak soils is shown in the example of the Northern Latitudinal Railway area.     

WACALIB version 3.3 — a computer program to reconstruct environmental variables from fossil assemblages by weighted averaging and to derive sample-specific errors of prediction

A computer program for reconstructing environmental variables (e.g. lake-water pH) from fossil assemblages (e.g. diatoms) by weighted averaging regression and calibration is described. The estimation of sample-specific errors of prediction by bootstrapping is outlined. The program runs on IBM-compatible personal computers.