Multiple discriminant analysis of the Drava River alluvial plain sediments

Three discriminant function models are raised and cross-compared in order to distinguish geochemical patterns characteristic for the Drava River floodplain sediments. Based on data representing total element concentrations in samples collected from alluvium (A), terrace (T), and unconsolidated bedrock (B) at the border of a floodplain, four element clusters emerged accounting for discrimination between the referred groups of sediments. The most prominent is contaminant/carbonate cluster characteristic for alluvium. The other two are: silicate cluster typical for unconsolidated geological substrate (Neogene sedimentary rocks); and naturally dispersed heavy metal cluster separating terrace from the former two groups. Models introducing depth intervals and single profiles as grouping criteria reveal identical sediment-heavy metal matrices. The second important issue of this paper is possibility of reclassification of samples originally assigned to one of the a priori defined groups of sediments, based on established geochemical pattern. The mapped geological units can be reconsidered by the post hoc assignments to a different group if geological border between alluvium and terrace or between terrace and bedrock can not be established geologically with absolute certainty.     

人工神经网络在地质学判别分析中的应用

将现代人工神经网络方法应用于地质学的判别分析，通过实例计算表明，无论是对于两组判别分析还是多组判别分析，人工神经网络方法都是切实可行的。     

1998年夏季南海水团分析

根据 1 998年夏季“南海季风试验 ( SCSMEX)”期间所获的 CTD资料 ,使用系统聚类、Fuzzy模式聚类、Bayes判别分析和 Fuzzy分析等水团分析方法 ,对南海水体的结构和水团配置状况等进行了分析 ,划出了南海存在的 9个主要水团 ,并对各水团的温、盐度特征进行了初析。在调查期间 ,南海本地水 (南海水 )几乎控制了整个调查海区 ,而黑潮水仅出现在台湾岛的西南海域 ;海水强烈混合发生在吕宋海峡附近 ;在中南半岛以东和吕宋岛以西海域 ,表层水明显下沉 ;在南海东南部可能有来自苏禄海的海水 ,其温、盐度特征类似于吕宋海峡中的黑潮水     

MW5型潜水电磁打捞器在桩孔施工中的应用

通过工程实例，介绍了应用潜水电磁铁处理桩孔施工中掉铁件事故的方法及操作要领，它具有实用价值。     

数学地质在矿山研究中的应用--判别分析归类矿体属性实例

数学地质方法为宏观地质研究提供了定量的可能。判别分析对研究未知属性矿体的归属有着重要的意义。苏州西部地区通安——潭山多金属成矿带中，小茅山铜铅锌矿段，潭山铅锌银铁矿段位于成矿带的两端，属性已知。该成矿带中有一未知属性的铜铅锌银迂里矿段，用判别分析方法对其归类。研究结果表明，未知属性的迂里矿段归属潭山多金属硫化物矿床类型，即高—中温多金属硫化物矿床类型。     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Logistic判别模型在强降水预报中的应用

利用Logistiv判别模型进行强降水预报,并设计3种方案进行对比分析。方案1直接使用14个影响因子进行判别预报,受因子共线性作用及噪音信号影响,虽然拟合效果较好,但预报效果明显下降。方案2对14个影响因子进行主成分分析,利用前6个主成分建模,虽然拟合效果较方案1降低,但由于消除了因子共线性作用以及噪音信号影响,预报效果较方案1提高。方案3运用Bootstrap抽样技术得到符干样本并建模计算模型参数,打乱了原有时间序列中的波动,仪保留平稳信息,拟合自由度进一步降低,导致拟合效果较方案案2下降,但预报效果却是3种方案中最好且最稳定的。在上述研究基础上,利用欧洲中心数值预报模式的预报场资料,建立基于Logistic判别模型的强降水客观预报系统,并在中央气象台业务运行。2013和2014年连续两年汛期预报检验结果表明,概模型对强降水预报的TS评分高于数值模式本身,具有一定的业务参考价值。     

降水井改造为原位生产井的工艺设计及应用

为有效地扩大油页岩原位开采区域和节约油页岩原位开采的成本,需将废置的降水井改造成为原位生产井。根据降水井与原位生产井井身结构的相似性,对农安油页岩原位开采先导试验工程基地的降水井改造工艺进行了设计和优化。同时研发了偏心轮式套管割管装置及三爪式套管打捞装置用于拔管施工。工程实践证明,该工艺具有步骤简单、合理、高效以及价格低廉等特点。偏心轮式套管割管装置及三爪式套管打捞装置应用在降水井改造中,均取得理想的效果。     

养殖曼氏无针乌贼(Sepiella japonica)的两性异形及判别模式的建立

两性个体的有效鉴别是曼氏无针乌贼高效繁育及养殖模式(如单性养殖)建立的基础。为研究曼氏无针乌贼主要形态指标及雌雄形态差异,测量了160只F1代曼氏无针乌贼的19项可测量性状及13项标准化性状。采用聚类分析、主成分分析和判别分析方法研究了曼氏无针乌贼同生群两性间的形态差异。结果表明:两性间可测量性状和标准化性状各有10项有显著性差异(P0.05);对标准化性状经过主成分分析和R-聚类分析均显示曼氏无针乌贼的两性的差异主要集中在辅助交配器官特征,头部特征,捕食器官和肥瘦特征以及体型特征等4个方面。在此基础上,通过逐步判别法从标准化性状中筛选出3项,即左3腕长/胴长、左4腕长/胴长及左触腕长/胴长,建立了雌雄的判别方程:雄性:F1=223.42X3+165.85X4+7.951X5–108.06;雌性:F2=151.48X3+92.71X4+19.19X5–60.38。所建立的判别方程,对样本群体的综合判别率达到95.00%。利用F2代亲体测量数据对判别函数进行识别验证,综合判别率达到85.34%。在判别方程中的3个标准化性状中,左3腕长/胴长和左4腕长/胴长在两性亲体间均存在显著的差异(P0.05),表明曼氏无针乌贼的雄性在性选择的作用下,交配器官存在显著差异。