松潘地区岩金统计成矿予测中泛克立格法的应用

川西北地区是国内重要的岩金普查靶区。本文对松潘地区的地质特征,泛克立格法原理,及在本区的应用情况作了介绍,对主要计算步骤,给出基本结果,对计算获得的有意义异常区,进行地质解释,并将此方法与趋势面分析进行对比。     

地球磁场对太阳风的加载响应与地震

地磁预报地震的方法越来越多,本文应用加卸载响应比方法,分析山西地区2009—2016年观测的加卸载响应比与山西地区4.0级地震之间的对应关系,初步给出适用于山西地区震例特征的磁异常判别指标。     

基于遗传算法的分类体系在地震预报中的应用探索

采用模似自然界生物进化过程的遗传算法的分类体系,并以首都圈地区为例,利用地震活动性资料的8种时空参数在地震预报中进行了应用探索．通过对1967—1992年间信息的学习演化,获得了20条拥有不同适应度的具有层次结构的地震预报规则集．对首都圈地区中等地震活动的预报规则进行了初步的分析讨论,发现了人们以往难于考虑周全的可能具有重要预报意义的异常组配.应用探索表明：在地震预报研究中应用具有自适应和潜在学习能力的基于遗传算法的分类体系,将可能推动人们对地震前兆及其物理机制的研究和探讨．     

利用IGS星历预报GPS卫星轨道

在动力学轨道拟合以及轨道积分的基础上,提出了基于IGS精密星历的GPS卫星轨道预报方法。该方法首先利用已知的IGS精密星历作为虚拟观测值,采用动力学方法拟合出GPS卫星的初始轨道和动力学参数,然后再通过积分来预报GPS卫星的轨道。主要讨论了基于不同弧段的IGS星历时,该方法对GPS卫星轨道的拟合和预报情况。研究结果显示:对于6 d弧段以内的IGS精密星历,其拟合轨道与IGS精密星历差值的三维RMS值均优于4 cm,随着拟合弧段的增加,拟合残差变大;当利用2～6 d弧段的IGS星历来预报GPS轨道时,大部分卫星第1天、第7天和第30天的三维预报精度可优于0.1 m、3 m和100 m。其中,2d弧段的IGS星历对GPS卫星第1天和第7天的预报结果最好,5 d弧段的IGS星历对GPS卫星第30天的预报结果最好。     

基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

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用前期海温预报四川夏季气温的EOF迭代方案

应用ＥＯＦ（经验正交函数）迭代方案，考虑多时次历史资料，在全球海区海温与四川盆地气温非同步联系的基础上，以海温为预报因子进行了夏季气温的长期预报。结果表明：西太平洋高温区等关键海区海温的异常对未来四川盆地夏季气温变化有重要影响，由此建立的引入多时次海温的ＥＯＦ迭代长期温度预报方法，具有较强的预报能力     

Predictability of Chinese Summer Extreme Rainfall Based on Arctic Sea Ice and Tropical Sea Surface Temperature

Chinese summer extreme rainfall often brings huge economic losses, so the prediction of summer extreme rainfall is necessary. This study focuses on the predictability of the leading mode of Chinese summer extreme rainfall from empirical orthogonal function(EOF) analysis. The predictors used in this study are Arctic sea ice concentration(ASIC) and regional sea surface temperature(SST) in selected optimal time periods. The most important role that Arctic sea ice(ASI) plays in the appearance of EOF1 may be strengthening the high pressure over North China, thereby preventing water vapor from going north. The contribution of SST is mainly at low latitudes and characterized by a significant cyclone anomaly over South China. The forecast models using predictor ASIC(PA), SST(PS), and the two together(PAS) are established by using data from 1980 to 2004. An independent forecast is made for the last 11 years(2005-2015). The correlation coefficient(COR) skills between the observed and cross-validation reforecast principal components(PC) of the PA, PS, and PAS models are 0.47, 0.66, and 0.76, respectively. These values indicate that SST is a major cause of Chinese summer extreme rainfall during 1980-2004. The COR skill of the PA model during the independent forecast period of 2004-2015 is 0.7, which is significantly higher than those of the PS and PAS models. Thus, the main factor influencing Chinese summer extreme rainfall in recent years has changed from low latitudes to high latitudes. The impact of ASI on Chinese summer extreme rainfall is becoming increasingly significant.     

基于机器学习模型的区域土壤重金属空间预测精度比较研究

为识别区域土壤重金属的空间变异特征并厘清其影响因素,本研究构建了多元线性回归（MLR）、弹性网络回归（ENR）、随机森林（RF）、随机梯度提升（SGB）、堆叠（stacking）集成模型、反向传播神经网络（BP-ANN）、基于模型平均的神经网络集成（avNNet）、线性核支持向量机（SVM-L）和高斯核支持向量机（SVM-R）共九种机器学习模型,利用山东省中部土壤重金属（Cd、Cu、Hg、Pb和Zn）和环境辅助变量数据,开展区域土壤重金属空间预测精度比较研究。结果表明：RF对五种重金属空间预测的决定系数（R²）介于0.263~0.448之间,平均绝对误差（MAE）和均方根误差（RMSE）分别小于8.408和10.636,预测值/实际值（P/O）均接近于1,对五种重金属的预测效果均较为理想,是研究区土壤重金属空间预测的最优模型;SVM-R整体预测性能仅次于RF,各项精度评价指标均相对稳健,可作为备选模型;其余七种模型的预测性能均明显低于RF和SVM-R。RF的空间预测结果显示,研究区五种重金属呈现出相似的空间分布格局,含量均由研究区东北部向西南部递减,包括东北部、北部和南部3个高值区,且高值区与当地工业–交通密集区的分布格局一致,反映出人类活动是研究区土壤重金属空间分异的主要影响因素。本研究可为区域土壤污染调查、评价和管控提供科学参考。     

Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.