Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added     

EnKF协方差膨胀算法对雷达资料同化的影响研究

基于集合卡尔曼滤波(EnKF)方法同化模拟雷达径向风和回波,引入具有时空自适应理论优势的贝叶斯膨胀算法,通过与常数膨胀算法的对比,分析了两种协方差膨胀算法对EnKF同化效果的影响。结果表明:在对流区域的北侧,由贝叶斯膨胀算法分析得到的回波在水平和垂直结构上均增强;在对流区域,由贝叶斯膨胀算法分析得到的各变量的集合离散度增大,均方根误差减小,水平和垂直速度增大,冷池强度减弱;模拟还发现贝叶斯膨胀算法提高了强对流系统的模拟效果,回波强度增强,阵风锋区内水平和垂直风速增大。这表明贝叶斯膨胀算法有效地改进了基于常数膨胀算法的EnKF同化雷达资料的效果。     

Sources of uncertainty in liquefaction triggering procedures

Uncertainties in observed data and in processing field and laboratory tests are major concerns. Assigning reasonable coefficients of variation to the parameters in the conventional analyses indicates that a site with deterministic factors of safety of 1.5 can actually have liquefaction triggering probability above 20%. About a third of the variance comes from uncertainty in the load, which is independent of the resistance. Researchers have traditionally presented the results of case studies in the form of charts showing instances in which liquefaction did and did not occur and have developed relations to separate the two. Although the original researchers developed the separations informally, recent work has applied statistical methods. These give the sampling distributions of the observed data rather than the probability of triggering given the data. Researchers have addressed this issue using Bayesian methods, adopting non-informative priors. Published curves of liquefaction probabilities can be interpreted as likelihood ratios. Other independent work demonstrates that geological, meteorological, and historical data can be used to develop prior probabilities, so it may not be necessary to assume a non-informative prior. The actual prior can then be combined with the likelihood ratios to provide rational probabilities of liquefaction. We recommend that researchers publish their likelihood ratios and allow engineers faced with particular sites to use those to update their own priors.     

基于多光谱影像和DEM的泥石流堆积扇识别研究——以白龙江流域武都段为例

选取中国泥石流多发区白龙江流域武都段作为研究区,在对该区域泥石流堆积扇的形态特征和堆积范围进行实地调绘的基础上,利用高分辨率影像(SPOT)进行目视解译,获得研究区部分泥石流堆积扇和非泥石流堆积区的分布范围,将其作为已知样本区。利用该区域多光谱遥感影像(ASTER)和数字高程模型(DEM),提取包含波段比和主分量的几十种特征指标。通过运用方差分析和聚类分析等方法对各指标进行分析计算,选取对区别泥石流堆积扇最具显著意义的指标进行输入,进而采用基于像元的分类方法识别泥石流堆积扇。得到如下结论:SPOT与ASTER融合影像的波段比、主分量指标可以有效地突出土壤岩石中的矿物成分,对泥石流堆积扇的识别具有显著意义;利用筛选出的遥感指标和地形指标作为输入,进行监督分类识别泥石流堆积扇,能够有效地将遥感指标和地形指标相结合,提取的堆积扇覆盖范围与实际情况较为接近。     

树分类器在测井岩性评价中的应用

 利用测井资料进行岩性定量识别,分类方法的设计是决定分类结果好坏的关键。本文讨论了树分类器与产生式规则相结合的方法在岩性分类中的应用。利用数据完整的测井曲线参与树分类器的学习、判别工作,用树分类器进行岩性的“逐级识别”,不完整的特征交由定性规则处理,既实现了岩性的细分,且保证了分类的精度。     

洪水的分类预测及优化调度

用系统聚类方法将历史洪水数据分成若干类型,并建立了模糊概率预测的隶属度回归方法。由此对未来洪水进行了分类概率预测,结果显示了来水过程具有一定的“类型可预报”的规律性。在此基础上讨论了一种Bayes实时优化调度规则并进行了数值模拟演算。     

基于Hopfield神经网络模型的遥感影像分类算法

针对遥感影像的分类特点，提出了一种基于Hopfield神经网络模型的遥感影像分娄算法。首先阐述了Hopfield神经网络的结构及其工怍原理，分析了Hopfield神经网络优化规则；然后在Hopfield神经网络通用模型基础上，实现了Hopfield神经网络的算法。实验结果表明，这种分类器具有较高的精度与效率，分类结果优于最大似然分类法。     

A Probabilistic Estimation of Strong Earthquakes'''' Occurrence in Aeghion Area, Central Greece

INTRODUCTIONTheexaminedareaislocatedintheGulfofCorinthandhasbeenrecognizedasoneofthemostactiveriftsinthewholeAegeanSea .Itsquaternarynormalfaulting (Sebriere ,1 977)anditshighseismicity (PapazachosandPapazachou ,1 997;AmbraseysandJackson ,1 990 )makeitaphysicallaboratorywithintheMediterraneanarea ,wherethephysicalprocessrelatedtotheseismiccyclecouldbestudied .ThecityofAeghionexperiencedastrong (MW =6 4 )earthquakeinJune 1 995,whichcausedseveredamage (Tselentis ,etal.,1 996 ) .Twent…     

铁岭组中风化柱状叠层石灰岩强度确定概率方法

研究区大量分布中风化柱状叠层石灰岩, 岩石强度具有很强的变异性和区域性, 而岩石强度是影响边坡稳定的重要因素。要进行边坡稳定性评价, 就需要对研究区现场试验数据作为随机变量进行概率统计分析, 获取可靠的岩石强度指标。本文通过对研究区内中风化柱状叠层石灰岩( t23 ) 现场点荷载实验数据进行统计分析, 利用x2 检验进行拟和, 得到岩石强度的概率密度函数和概率分布形式, 抗压强度和抗拉强度均服从对数正态分布。在此基础上, 结合相近岩性岩石强度的拟和结果, 建立区内中风化柱状叠层石灰岩点荷载试验强度概率分布函数。运用Bayes估计推断区域内中风化柱状叠层石灰岩岩石点荷载强度, 得到岩石抗压强度预测值为72. 42 MPa, 抗拉强度预测值为2. 29 MPa, 相对误差较小, 样本信任度达到70%以上。该方法对于岩石强度估计是有效的, 由该方法所得到的估计值对研究区边坡稳定性评价以及后期边坡治理都具有重要的实践意义。