大数据环境下卫星对地观测数据集成系统的关键技术

建立卫星对地观测数据集成系统是遥感卫星数据信息资源有效管理与应用的重要手段。从我国对地观测重大需求以及前沿科学问题入手,提出大数据环境下卫星对地观测数据集成系统建立中亟待解决的关键技术,包括大容量异构对地观测数据集成的语义技术、基于网格的遥感图像快速处理技术、遥感大数据深度分析技术、多数据中心协同处理及云平台技术,为实现集成卫星图像、地面观测数据和模拟模型的元数据管理、几何精度纠正和卫星数据质量评价、海量卫星图像数据的空间分析与知识发现、分布式高性能卫星图像数据管理和归档等基本功能,为解决海量卫星数据分布式存储与计算、数据集成与互操作、空间数据分析与地学知识发现提供新思路、新技术与新方法。     

Research challenges and opportunities in mapping social media and Big Data

Social media and Big Data have transformed our world into interconnected cyberspace and realspace. Cartographers can now trace, monitor, and map the spread of social movements, disease outbreaks, nature hazards, and popular events by digitally collecting social media and Big Data with locational contents, such as global positioning system tags and user location profiles. The dynamic characteristics of social media and Big Data provide a great research opportunity for cartographers to map and analyze human behaviors, communications, and movements. However, there are many challenges and pitfalls in cartographic research associated with spatiotemporal analysis of social media contents and Big Data. This short paper will address important research challenges and major opportunities for cartographers to process and visualize Big Data and social media.     

Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS

New sources of geolocated information, associated with big data and social networks, show great promise for geographical research, especially in the field of tourism geography. Photo-sharing services comprise one of these sources. The aim of this article is to demonstrate the potential of photo-sharing services for identifying and analyzing the main tourist attractions in eight major European cities: Athens, Barcelona, Berlin, London, Madrid, Paris, Rome and Rotterdam. Geotagged photographs on Panoramio were differentiated according to whether they had been taken by tourists or local residents, and their spatial distribution patterns were analyzed using spatial statistical techniques in a GIS. The results indicated the concentration and dispersion of photographs in each city and their main hot spots, and revealed marked differences between tourists' and residents' photographs, since the former showed higher spatial concentrations. In addition, differences were observed between cities; Barcelona and Rome presented a strong spatial concentration compared with London or Paris, which showed much greater dispersion.     

Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.     

Landslide identification using machine learning

Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,landslide-related data are compiled,including topographic data,geological data and rainfall-related data.Then,three integrated geodatabases are established;namely,Recent Landslide Database(Rec LD),Relict Landslide Database(Rel LD)and Joint Landslide Database(JLD).After that,five machine learning and deep learning algorithms,including logistic regression(LR),support vector machine(SVM),random forest(RF),boosting methods and convolutional neural network(CNN),are utilized and evaluated on each database.A case study in Lantau,Hong Kong,is conducted to demonstrate the application of the proposed method.From the results of the case study,CNN achieves an identification accuracy of 92.5%on Rec LD,and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing.Boosting methods come second in terms of accuracy,followed by RF,LR and SVM.By using machine learning and deep learning techniques,the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.     

地球化学场及其在隐伏矿体三维预测中的作用

本文从物理学"场论"的角度介绍了地球化学场的概念,并从场源、场作用及地球化学指标分布三个基本要素出发阐述了地球化学场的扩散、对流-扩散的动力学机制。首先,按照采样介质,将地球化学场分为原生的岩石地球化学场,以及次生的土壤、水系沉积物、水文和气体地球化学场,描述了组成各类地球化学场的要素。其次,将地球化学场的分析方法概括为静态的空间结构分析和动态的时空结构分析两种,提出要以场的动力学机制为基础,利用地球物理学中的正、反演理论来研究四维时空中地球化学场的发展和演化。最后,探讨了地球化学场时空结构分析与三维地学模拟两者之间彼此补充和相互验证的关系,三维地学模拟构建的场源及空间介质模型,为地球化学场的正、反演提供了初始条件,地球化学场反演的结果又可用来修正三维地质模型;探讨了地球化学场与大数据分析技术间的关系,即采用大数据的"数据驱动"的思路来挖掘其与多元地学数据之间的隐性联系,探索其与成矿过程的关联性。地球化学场与三维地学模拟、大数据分析技术的结合将为隐伏矿体三维预测中地下成矿物质的分布和演化提供依据。     

地震前兆观测中的大数据挖掘应用探讨

大数据挖掘研究与应用是当今的热点问题。本文简要概括了大数据研究的基本思想,分析了地震监测中前兆观测数据大数据研究的必要性,并设计了几种前兆观测大数据研究在地震行业内部的可能应用方向和具体应用问题,以期能够推动前兆观测数据利用大数据研究思想的创新应用和研究工作。     

滇西北普朗斑岩铜矿与成矿有关的花岗岩与全球埃达克岩的对比:大数据研究的初步结果

云南格咱岛弧是西南三江构造-岩浆岩带中义敦岛弧的重要组成部分,位于义敦岛弧带的南端,是中国西部地区一个重要的铜铅锌多金属成矿带和印支期斑岩铜矿带,其中普朗斑岩型铜矿是印支期斑岩铜矿的典型代表。本文在前人研究基础上,采用大数据分析技术,对普朗斑岩铜矿与成矿有关的花岗岩与全球埃达克岩进行了对比研究,得到506个元素组合,获得交叠率比值127765个。结果表明,普朗斑岩与全球埃达克岩的地球化学数据基本上重叠,具有与全球埃达克岩类似的特征;但Cu、Mo、Pb、Zn成矿元素明显超出全球埃达克岩的分布范围,REE和Ga与主元素的比值虽然在全球埃达克岩范围内,却显示了位于全球埃达克岩某个端元的现象。另外,与斑岩铜矿有关的埃达克岩分布于全球埃达克岩范围的局部,数据部分或大部分逸出了全球埃达克岩分布的范围,具有低Sr/Cu、Ta/Cu、Al/Mo、Mn/Zn、Mn/Cu、Mn/Mo以及Hf、K、Ti、P、Co、Th、Ca/Cu比值的特征,数据点偏离全球埃达克岩范围越远,蚀变与矿化作用越强烈,与铜钼成矿关系越密切。本文地球化学数据挖掘结果可以作为找矿标志来使用,这为普朗铜矿床深部及外围的地质找矿提供了新的科学依据。     

地质大数据的特点及其在成矿规律、成矿系列研究中的应用

进入21世纪以来,面对这一信息大爆炸的时代(即"大数据"时代),人们的生活、工作与思维都面临着大变革。文章基于"大数据"思维,探讨了成矿规律及成矿系列研究中的一些问题,以期地质大数据能够在成矿规律及成矿系列的研究中得到更好地应用。首先,从"大数据"的特点及其研究现状入手,结合地质矿产研究中的现实问题,阐述了地质大数据的概念及其外延。然后,综合"大数据"与成矿规律、成矿系列研究相关各地质专业的特点,浅析了矿产资源领域地质大数据的10个特点;其中,除了从地质矿产的视角解释了"大数据"的大量性、高速性、多样性、价值性4大特点外,还基于地质矿产专业提出了地质大数据的6大新特点:"物质性与非物质性""空间性与非空间性""时间性与非时间性""因果性与非因果性""主体性与非主体性"及"客体性与非客体性",并在"大数据"的背景下作了新的诠释。最后,总结了地质大数据在成矿规律、成矿系列、成矿体系研究中的应用情况及注意事项,期望地质大数据能为成矿理论和成矿预测工作提供新思路。     

基于移动通信大数据的地震灾区人口快速处理系统研究

破坏性地震发生后,及时准确掌握被困人数和具体位置是救援的关键。传统地震应急基础数据库的人口数据是某个时期以行政单元或者公里格网为基础的统计数据,缺乏时效性和空间分布特征,难以据此准确评估地震灾区被困人员的分布情况。该研究根据移动通信定位技术中的被动Cell-ID定位法原理,获取研究区手机定位数据,系统以Microsoft Visual Studio 2012为开发平台,运用C#语言和ArcEngine开发组件来构建,对获取的手机定位数据进行处理,评估灾区被困人口的分布情况,制作专题图,为震后的应急救援提供参考依据。