基于地貌单元的小区域地质灾害易发性分区方法研究

以汶川县城周边区域为研究区,分别以栅格单元与地貌单元作为单位评价单元,以信息量法与逻辑回归法两种评价模型对研究区进行地质灾害易发性评价分区。根据对评价结果的比较分析,在小区域范围内,基于地貌单元的区域易发性分区不仅仅能够更好地体现出区域内局部综合特性,而且评价分区结果与地质灾害实际分布情况更加吻合,分级层次更加明显,数学模型的适用效率很好。由此可见,在小范围区域内,基于地貌单元的地质灾害易发性评价分区具有良好的适用性与可塑性,在大比例尺地质灾害易发性和危险性制图中是一个有益的尝试与启发。     

第三系砂岩波速特性探讨及统计分析

波速测试技术作为一种简便、快捷的测试技术被广泛应用于各种工程实践中, 第三系砂岩是非常具有代表性的软岩, 对第三系砂岩波速测试的研究具有重要意义。本文以吉阳核电厂址的第三系砂岩为研究对象, 通过整理单孔波速、跨孔波速方法获得的参数, 分析半成岩岩体波速的空间分布规律及特性, 寻找波速与埋深深度、标贯动探参数之间的关系。结果表明单孔、跨孔波速测试波速值随深度增加而增加, 埋深5m以上增加幅度明显, 埋深5m以下增加趋势减缓, 平面上不同位置各孔的波速值基本一致, 说明第三系半成砂岩岩体分布较均匀。剪切波速与埋置深度线性回归拟合效果较好, 对数拟合优度在0.95以上, 精度最高, 相对可靠, 剪切波速与标贯击数之间相关性不明显, 与动探击数之间基本不相关。     

吐鲁番市春季灾害性大风的特征及预报方法

在对吐鲁番市春季灾害性大风的天气、气候特征进行分析的基础上,通过对大风出现同时刻历史资料的普查,找出预报因子,建立权重回归和二级判别方程,利用欧洲中心数值预告产品和本站实时资料用PP法做灾害性大风的有、无和强度预报.同时也是在无自绘图只有传真图的情况下进行要素客观预报的一种尝试.     

新型热压WC-Fe基金刚石钻头胎体性能研究

为了提高钻头在坚硬致密岩层中的钻进效果并降低钻头成本,将传统的热压碳化钨（WC）基金刚石钻头胎体中一部分WC用Fe代替。用粉末冶金的方法烧制胎体试块并测试其性能。通过对比性能测试,分析了碳化钨和铁的含量变化对胎体性能的影响规律。应用回归分析和规划求解,分别建立了硬度和耐磨性的回归方程,确定了最优的胎体硬度和耐磨性以及对应的胎体配方。     

基于PCA法的春、夏季东山湾海域富营养化特征

根据2011年5月和8月东山湾及邻近海域的调查结果,选择10个与富营养化有关的参数,包括溶解无机氮(DIN)、溶解硅酸盐(DSi)、活性磷酸盐(SRP)、总氮(TN)、总磷(TP)、化学耗氧量(COD)、溶解有机碳(DOC)、总有机碳(TOC)、叶绿素a(Chl-a)和浮游植物丰富度(d)等,应用主成分分析(PCA)法对该海域富营养化特征进行研究。主成分分析表明,主成分1(PC1)表征了有机污染、浮游植物和总磷状况;主成分2(PC2)反映了硅和氮营养水平,主成分3(PC3)体现了SRP的特点。春、夏季PC1、PC2、PC3和综合主成分(CPC)的空间分布表明漳江口和八尺门养殖区邻近海域富营养化风险较高;各主成分与盐度的相关分析表明东山湾内富营养化压力主要来自漳江,夏季有所增强;有机污染物和浮游植物可能是控制东山湾海域春、夏季富营养空间分布的主要驱动因素。     

Robust LS-SVM regression for ore grade estimation in a seafloor hydrothermal sulphide deposit

Due to the geological complexities of ore body formation and limited borehole sampling, this paper proposes a robust weighted least square support vectormachine (LS-SVM) regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each in complete sample. Then, a weighted K-nearest neighbor (WKNN) algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimization (PSO) algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by randomdivision. Coupled simulated annealing (CSA) and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight (IDW), ordinary kriging (OK), and back propagation (BP) neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the othermethods, and has strong predictive and generalization ability.     

上海沿海极大风速预报方程的建立和应用

运用统计分析方法,对上海沿海3个浮标站的逐小时的2min平均风速和极大风速进行分析,发现两种风速具有很好的线性关系。通过建立两种风速的回归方程,获得了估计极大风速的客观方程,对3个浮标站,单站一元一次回归方程都能很好得反映2 min平均风速和极大风速的关系,实测极大风速与拟合极大风速的平均绝对误差均小于1 m/s;3个浮标的拟合数据都是以正偏差为主;3个浮标拟合偏差2 m/s的样本数都在5%以下。利用WRF模式预报风速进行极大风速试报,结果表明,3个浮标实测极大风速与预报极大风速的平均绝对误差均小于2 m/s,偏差4 m/s的样本数在10%以下,TS评分均在74分以上,建立的极大风速客观预报方程的效果比较理想。     

基于WOA-LSSVM的海底沉积物物性参数建模

电阻率测量是海底沉积物工程地质勘察的主要原位观测方法之一,作为一种间接测量方法,需要建立沉积物物性参数与沉积物电阻率的回归模型。为提高建模精度,本文提出了一种基于鲸鱼算法优化的最小二乘支持向量机(Whale Optimization Algorithm-Least Squares Support Vector Machine,WOA-LSSVM)的海底沉积物物性参数与电阻率回归建模方法。该方法建立了海底沉积物电阻率与沉积物4种基本物性参数(含水率、密度、孔隙比、塑性指数)的单输入、单输出最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)回归模型,利用WOA算法对LSSVM参数进行寻优取值。对比研究了WOA算法、遗传算法(Genetic Algorithm,GA)、粒子群(Particle Swarm Optimization,PSO)算法优化的LSSVM建模结果,结果表明,基于WOA-LSSVM建立的海底沉积物物性参数与电阻率的回归模型具有更好的预测效果,均方根误差降低1.1%～14.9%,平均绝对百分比误差降低0.4%～19.9%。     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.