精细积分在冰荷载识别中应用

冰激振动中引起的动冰力一直是很受关注的问题。根据精细积分法的基本原理,假定外荷载在离散时间步长内按线性变化,给出了一种冰载荷时域识别方法。经数值算例仿真以及在室内开展的冰激桥墩振动模型试验冰荷载反演的结果表明,该方法形式简洁明了,计算量小,应用方便,特别适宜于计算机求解,具有一定的理论意义和实用价值。     

一维海冰热力模式的守恒型差分格式和数值模拟

根据前人研究的一维海冰热力模式，考虑到方程的特点，采用积分插值法构造了守恒型差分格式，并对海冰热力模式所描述的冰内热扩散传导和冰底厚度的变化进行了数值模拟。当冰面覆盖积雪时，用该解法同样计算了雪层内的热扩散，考虑冰雪之间的热力相互作用后，得到冰厚在有雪盖情况下的变化。     

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%。     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。     

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.     

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 check 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 reflected 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 R2, Somers'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.     

黄河利津水文站不同粒径悬浮颗粒物中有机碳含量的研究

估算不同粒径TSS中POC的入海日通量,对黄河利津水文站悬浮颗粒物(total suspended solid,TSS)样品,采用基于stokes原理的沉降法分为Ⅴ级,对分级后的样品测其有机碳(particulate organic carbon,POC)含量和粒度组成。在此基础上,用多元线性回归法对数据进一步处理,准确得到样品中不同粒径TSS中POC的含量。研究表明,黄河口淡水端水体TSS,<8μm的粘土和极细粉砂、8~16μm的细粉砂中,POC的含量为0.600%和0.400%左右;16~32μm的中粉砂中,POC的含量不足0.200%;而32~63μm的粗粉砂和>63μm砂中POC含量仅为0.050%和0.004%左右,80%以上的POC主要集中在<16μm的TSS中,而<32μm的TSS承载了95%以上的POC。     

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。