中国地质统计学（空间信息统计学）发展的回顾与前景

讨论了地质统计学（空间信息统计学）发展的若干问题，其内容包括４部分：１．地质统计学的理论及方法；２．关于软件系统的现状；３．关于实际应用；４．地质统计学发展前景的若干看法。     

普通克里格法在矿产储量计算中的应用

普通克里格法能充分利用样品信息，有效地提高块段平均品位及矿产储量精度。以刁泉银铜矿为实例，介绍了该方法的理论和方法应用以及储量计算流程图。     

济南、福山太阳辐射的气候学特征分析

利用济南、福山地面太阳辐射资料，分析了太阳辐射的气候变化特征。结果表明，60到90年代，济南地面太阳总辐射量、直接辐射量呈显著减少趋势，福山变化趋势不明显；90年代以后，济南地面太阳总辐射呈增多趋势。     

第二松花江流域水电厂综合中长期水文预报的分析研究

基于对第二松花江流域上游小山、松山、两江水电站中长期水文预报研究成果，论述了综合中长期水文预报的研究思路和定性预报、定量预报的分析研究方法。该研究通过2002年实践检验，具有较高的预报精度。     

鲁西南深埋粘性土物理力学指标多元统计研究

运用R因子分析和R型聚类两种多元统计分析方法,对鲁西南地区深埋粘性土的20组样的8个物理力学指标数据进行了分析。利用R因子分析方法,揭示了各指标对鲁西南地区深埋粘性土状态和力学性质的影响关系。利用R型聚类分析方法,研究了深部粘性土各物理力学指标之间的相关性,为进一步从物理力学指标对深埋粘性土的特殊状态和力学特性的研究打下基础。     

Indicator Simulation Accounting for Multiple-Point Statistics

Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features. Connectivity of high and low values is often critical for grades in a mineral deposit. Multiple-point statistics can help to characterize these features. The use of multiple-point statistics in geostatistical simulation was proposed more than 10 years ago, on the basis of the use of training images to extract the statistics. This paper proposes the use of multiple-point statistics extracted from actual data. A method is developed to simulate continuous variables. The indicator kriging probabilities used in sequential indicator simulation are modified by probabilities extracted from multiple-point configurations. The correction is done under the assumption of conditional independence. The practical implementation of the method is illustrated with data from a porphyry copper mine.     

应用空间统计学理论解译遥感影像信息“缺失”区

借助图像处理软件ERDAS、地理信息系统软件ArcInfo以及空间统计分析软件ILWIS ,在对TM遥感影像进行分类的基础上 ,运用空间统计学理论以及Kriging插值技术 ,内插了影像真实信息“缺失”斑块的信息 ,插值结果通过了精度检验。为解译影像信息“缺失”区 ,提供了一种手段和方法。     

Determining the maximum degree of harmonic coefficients in geopotential models by Monte Carlo methods

Random errors for the harmonic coefficients of a geopotential model are generated from the matrix of normal equations by a parallel computer applying the Gibbs sampler. This leads to random values for the harmonic coefficients. They are transformed by nonlinear, quadratic transformations to random values for the square roots of degree variances, of mean squares of geoid undulations and gravity anomalies. The expected values of these quantities are not equal to the values of these quantities computed by the estimated harmonic coefficients, due to correlations and errors in the estimation. By hypothesis tests estimated harmonic coefficients distorted by correlations and errors are detected. Applying the tests to the geopotential model ITG-CHAMP01 of the Institute of Theoretical Geodesy in Bonn it is concluded that above the degree 62 the harmonic coefficients cannot add any information to the geopotential model.     

Critical rainfall statistics for predicting watershed flood responses: rethinking the design storm concept

Recent advances have been made to modernize estimates of probable precipitation scenarios; however, researchers and engineers often continue to assume that rainfall events can be described by a small set of event statistics, typically average intensity and event duration. Given the easy availability of precipitation data and advances in desk‐top computational tools, we suggest that it is time to rethink the ‘design storm’ concept. Design storms should include more holistic characteristics of flood‐inducing rain events, which, in addition to describing specific hydrologic responses, may also be watershed or regionally specific. We present a sensitivity analysis of nine precipitation event statistics from observed precipitation events within a 60‐year record for Tompkins County, NY, USA. We perform a two‐sample Kolmogorov–Smirnov (KS) test to objectively identify precipitation event statistics of importance for two related hydrologic responses: (1) peak outflow from the Six Mile Creek watershed and (2) peak depth within the reservoir behind the Six Mile Creek Dam. We identify the total precipitation depth, peak hourly intensity, average intensity, event duration, interevent duration, and several statistics defining the temporal distribution of precipitation events to be important rainfall statistics to consider for predicting the watershed flood responses. We found that the two hydrologic responses had different sets of statistically significant parameters. We demonstrate through a stochastic precipitation generation analysis the effects of starting from a constrained parameter set (intensity and duration) when predicting hydrologic responses as opposed to utilizing an expanded suite of rainfall statistics. In particular, we note that the reduced precipitation parameter set may underestimate the probability of high stream flows and therefore underestimate flood hazard. Copyright © 2016 John Wiley & Sons, Ltd.     

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

The most popular practice for analysing nonstationarity of flood series is to use a fixed single‐type probability distribution incorporated with the time‐varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time‐varying under changing environments. To allow the investigation of this complex nature, the time‐varying two‐component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta‐heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single‐type distributions, stationary mixture distributions and time‐varying single‐type distributions. The results highlighted the advantages of TTMD with physically‐based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. Copyright © 2016 John Wiley & Sons, Ltd.