分布式数据库Greenplum在地震前兆数据存储中的应用     

陈晓琳  李盛乐  刘坚  刘珠妹 《地震研究》2020,(2):412-416,418

随着地震前兆观测台网的加密、采样率的提高,地震前兆观测数据量也在快速增加。在进行地震数据共享服务时,需要快速获得大量数据集,无疑对前兆共享数据库的数据处理能力提出了更高的要求。针对这一问题,提出基于Greenplum数据库的地震前兆数据存储设计方案。通过搭建Greenplum分布式数据库环境,实现了海量前兆数据的快速处理,并与传统Oracle数据库进行对比,结果表明:Greenplum数据库读取前兆数据耗时更低,对于大批量数据的读取操作,Greenplum数据库的优势更加明显;Greenplum数据库良好的可扩展性和对应用编程接口(JDBC、ODBC)的支持,使得其在前兆数据分析处理中的应用前景广阔。  相似文献   

地磁台站勘选数据处理系统设计     

王莉森  张云昌  殷金平  王江 《地震地磁观测与研究》2020,41(4):217-224

为实现地磁台站场地勘选指标的快速计算和勘选报告的在线保存与查询，提高地磁台站勘选数据处理效率，降低人工处理数据错误率，在现有地磁数据处理软件功能的基础上设计开发地磁台站勘选数据处理系统，以提高勘选的工作效率。  相似文献   

青海省新一代BDS高精度基准服务平台的构建          下载免费PDF全文

李延龙  张生鹏  张永荥 《全球定位系统》2020,45(2):91-97

介绍了青海省新一代北斗卫星导航系统(BDS)高精度基准服务平台.该平台具备用户管理、数据处理、空间环境监控、高精度定位等功能,可以满足系统管理员、行政领导、单位用户和实时动态(RTK)测量用户在内的各种用户的多样化需求,并且遵循了管理标准化、可扩展性强、现势性好、实用性好、安全性强、具有前瞻性等建设原则.  相似文献   

“数字海底”--海底数据集成管理与可视化研究     

肖鹏  王悦东  李安龙 《海洋信息》2020,35(1):40-45,51

针对目前"数字海底"建设中存在海底调查数据集成管理与三维可视化表达不足等问题,本文采用GIS技术,以黄河水下三角洲埕北海域为研究对象,利用地理空间数据集成理论与三维建模方法,建立了埕北海域三维海底空间数据库,实现了研究区域地形数据、地层数据、钻孔数据、表层沉积物数据的有效组织管理与可视化,并在此基础上,采用ArcGIS Engine 10.0,结合.NET平台,在Visual Studio 2010开发环境下,利用C#语言进行了二次开发,实现了基于C/S (Client/Server)架构的三维海底虚拟仿真系统的开发,设计了一套面向埕北海域的三维海底虚拟仿真原型系统。  相似文献   

Establishment and application of an intelligent treating method for oil spill identification     

TAN Liju  ZHAO Ruxiang  YIN Xiaonan  ZHANG Haijiang  WANG Jiangtao 《海洋学报(英文版)》2018,37(11):116-122

In the identifying process of an oil spill accident, manual integral and artificial visual comparison are commonly used at present to determine the oil spill sources, these methods are time-consuming and easily affected by human factors. Therefore, it is difficult to achieve the purpose of rapid identification of an oil spill accident. In this paper, an intelligent method of automatic recognition, integration and calculation of diagnostic ratio of Gas Chromatography-Mass Spectrometer (GC/MS) spectrum are established. Firstly, four hundreds of samples collected around the world were analyzed using a standard method and Retention time locking technology (RTL) was applied to reduce the change of retention time of GC/MS spectrum. Secondly, the automatic identification, integration of n-alkanes, biomarker compounds, polycyclic aromatic hydrocarbons and calculation of the diagnostic ratios were realized by MATLAB software. Finally, a database of oil fingerprints were established and applied successfully in a spill oil accident. Based on the new method and database, we could acquire the diagnostic ratios of an oil sample and find out the suspected oil within a few minutes. This method and database can improve the efficiency in spilled oil identification.  相似文献   

基于深度学习的目标检测定位系统设计与实现     

王俊强  李建胜  程相博  杨戬峰 《测绘与空间地理信息》2020,(1):133-136,144

针对传统遥感影像目标检测方法效率不高,并且无有效手段对检测信息进行管理利用的问题,提出了在B/S构架下基于深度学习的目标检测及定位方法。通过集成深度学习框架、WebGIS以及数据库,实现了集遥感影像目标检测、展示及管理于一体的目标检测定位系统,满足多用户基于前端浏览器的并发目标检测需求。利用网格划分策略,实现了基于前端的大区域范围的目标快速检测。基于某机场飞机目标及某城市区域运动场目标检测结果表明:本文设计的目标检测定位系统能够在前端实现目标快速检测定位,具有较高检测精度,并可有效管理检测信息,为深度学习循环再利用提供数据支撑。  相似文献   

环境水样中农药污染分析技术研究进展   总被引：2，自引：2，他引：0  

郭婕  张燕  胡振国  刘菲 《岩矿测试》2021,(1):16-32

随着农业集约化和城市化的推进,世界上大量水环境中农药残留量已超过规定的限值,水环境中农药污染问题受到社会各界的广泛关注。作为世界上最大的农药生产国和使用国,中国水环境中农药残留量远高于其他发达国家,已有研究表明在我国七个典型流域(长江、太湖、黄河、松花江、黑龙江、大运河和东江)中检测到19种农药,平均浓度范围为0.02～332.75ng/L。农药及其转化产物对生态环境和人体健康具有潜在威胁,水环境中农药残留的研究是水质评估中必不可少的组成部分,而靶向筛查难以检测未知农药及其转化产物。因此,环境中农药残留及其转化产物的非靶向筛查亟需完善。本文依据农药组分非靶向筛查的分析流程,对近五年水质样品中农药残留靶向及非靶向筛查方法进行综述,梳理了近年来国内外食品与水环境中农药残留限量的相关法律法规,对水环境中农药残留分析方法的研究进展进行概述;总结了液液萃取(LLE)、固相萃取(SPE)、固相微萃取(SPME)等样品前处理方法的特点,在这些方法中,固相萃取是农药非靶向筛查的主要前处理方法,具有良好应用前景。本文还探讨了分析仪器从色谱检测到色谱-质谱联用的发展趋势,多种高分辨率质谱的产生为农药非靶向筛查提供了多层次的分析需求;同时通过总结近年来农药筛查确证相关的指导标准、质谱数据库与多种鉴定方法,指出水环境中农药污染分析技术的发展趋势。  相似文献   

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer   总被引：1，自引：0，他引：1  

Wei Chen  Xi Chen  Jianbing Peng  Mahdi Panahi  Saro Lee 《地学前缘(英文版)》2021,12(1):93-107

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks     

Husam A.H.Al-Najjar  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(2):625-637

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献   

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management   总被引：1，自引：0，他引：1  

Zizheng Guo  Yu Shi  Faming Huang  Xuanmei Fan  Jinsong Huang 《地学前缘(英文版)》2021,12(6):243-261

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献