Hotshots of Spatio-temporal Behavior of Chinese Residents in the Context of Big Data:Visual Analysis Based on CiteSpace

By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.

Quantitative Geoscience and Geological Big Data Development： A Review

After long-term development, mathematical geology has today become an independent discipline. Big Data science, which has become a new scientific paradigm in the 21st century, gives rise to the geological Big Data, i.e. mathematical geology and quantitative geoscience. Thanks to a robust macro strategy for big data, China＇s quantitative geoscience and geological big data＇s rapid development meets present requirements and has kept up with international levels. This paper presents China＇s decade-long achievements in quantitative prediction and assessment of mineral resources, geoscience information and software systems, geological information platform development, etc., with an emphasis on application of geological big data in informatics, quantitative mineral prediction, geological environment and disaster management, digital land survey, digital city, etc. Looking ahead, mathematical geology is moving towards ＂Digital Geology＂, ＂Digital Land＂ and ＂Geological Cloud＂, eventually realizing China＇s grand ＂Digital China＂ blueprint, and these valuable results will be showcased on the international academic arena.

Exploration and Realization of Several Key Problems of Geological Big Data

With the rapid development of technology,geological big data is increasing explosively,and plays an increasingly important position in the national economy(Zhang and Zhou,2017;Zhou et al.,2018).Governments and agencies attach great importance to the open internet service of geological big data and information at home,and abroad(Yan et al.,2013;Guo et al.,2014).The basic norms of western countries’geological data information services are rich and varied products.

Construction of Smart City Spatio-Temporal Information Cloud Platform in Weifang,China

On the basis of the digital Weifang geospatial framework,Smart Weifang spatio-temporal information cloud platform(WFCP)integrated legal person information,population,place name and address data,macroeconomic data and so on.And it also expanded the data contents,such as the indoor and outdoor data,the overground and underground data,panoramic data and real data.It also introduced the contents of historical geographical information in different periods and real-time location information,address information of sensing equipment,real-time perception and interpreting information.It has overcome the difficulties of real-time access of Internet of Things(IoT)perception,multi-node collaboration,64-bit support,cluster deployment and has the characteristics of spatio-temporal management,ondemand service,large data analysis and micro-service architecture.It built spatio-temporal information big data center and spatio-temporal information cloud platform,realized the convergence and management of the distributed big data,deeply applied for land,transportation,environmental protection,police and subdistrict five areas,by supporting the integrated application of multi-source information and supporting intelligent deep application.In the aspect of hardware environment construction,according to the top-level design and unified arrangement of Smart Weifang,the WFCP was migrated to Weifang cloud computing center,to achieve the on-demand computing resources and dynamic scheduling load-based computing resources,to support the generalizing load map application.

STGI:a spatio-temporal grid index model for marine big data

Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.

HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems

With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming;the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.

基于地质大数据的中国金矿时空分布规律定量研究

在大数据科学成为新的科学范式的背景下,基于地质大数据理念,将传统的定性地质研究方法推向定量研究的高度。文章通过对5300余处金矿产地资料的系统梳理,定量分析了中国省(自治区)、市、县级及Ⅲ级成矿区带金矿成矿密度、成矿强度和各成矿时代金矿的成矿强度问题。研究表明,中国金矿具有明显的时空集中分布趋势。中国金矿空间分布具有区域性集中的明显趋势,以胶东、小秦岭等区域为金矿高密度、高强度成矿区;新疆矿床数量最多;山东省金矿成矿密度最大,成矿强度最强;在地级市统计中,山东烟台市和河北承德市是全国仅有的两处矿产地数量过百的地级市,山东烟台市的金矿无论是矿产地数量、成矿密度,还是成矿强度均居全国首位;在县级行政区统计中,新疆塔城地区托里县金矿矿产地数量最多(52处),安徽铜陵市区金矿成矿密度最大,山东烟台市莱州市的金资源储量最大(2341 t),成矿强度最强(1.35 t/km^(2));在成矿区带统计中,华北陆块北缘东段成矿带(Ⅲ-57)金矿矿产地数量最多(345处),胶东成矿带(Ⅲ-65)金矿成矿密度最大,成矿强度最强。中国金矿时间分布不平衡,具有成矿强度老弱新强、东西南北成矿有别、叠加成矿时间跨度大和新生代金矿资源潜力大的特点;燕山期是中国金矿最重要的成矿时期,成矿强度大,达到10.5个矿区/Ma,金资源储量93 t/Ma。今后中国金矿地质工作的核心依然是加强金矿找矿工作、保障国家金融安全。

A study on geological structure prediction based on random forest method

The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt,which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt.At present,there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt.One of the reasons is that the genetic tectonic setting of the Carboniferous volcanic rocks is not clear.Due to the diversity of volcanic rock geochemical characteristics and its related interpretations,there are two different views on the tectonic setting of Carboniferous volcanic rocks in the Xingmeng orogenic belt:island arc and continental rift.In recent years,it is one of the important development directions in the application of geological big data technology to analyze geochemical data based on machine learning methods and further infer the tectonic background of basalt.This paper systematically collects Carboniferous basic rock data from Dongwuqi area of Inner Mongolia,Keyouzhongqi area of Inner Mongolia and Beishan area in the southern section of the Central Asian Orogenic Belt.Random forest algorithm is used for training sets of major elements and trace elements in global island arc basalt and rift basalt,and then the trained model is used to predict the tectonic setting of the Carboniferous magmatic rock samples in the Xingmeng orogenic belt.The prediction results shows that the island arc probability of most of the research samples is between 0.65 and 1,which indicates that the island arc tectonic setting is more credible.In this paper,it is concluded that magmatism in the Beishan area of the southern part of the Central Asian Orogenic belt in the Early Carboniferous may have formed in the heyday of subduction,while the Xingmeng orogenic belt in the Late Carboniferous may have been in the late subduction stage to the collision or even the early rifting stage.This temporal and spatial evolution shows that the subduction of the Paleo-Asian Ocean is different from west to east.Therefore,the research results of this paper show that the subduction of the Xingmeng orogenic belt in the Carboniferous has not ended yet.

山东蒙阴金刚石矿床地质大数据三维建模与成矿预测

大数据时代的到来,需要我们在大数据思维指导下进行三维地质建模工作。笔者以山东蒙阴西峪金刚石矿作为为研究对象,基于大数据发现技术与大数据挖掘技术实现了传统三维地质建模工作和信息技术的深度融合,结合可视化表达等技术方法充分提取了文本数据中西峪金刚石矿的成因模式信息与地球物理勘探标准信息;并在此指导下完成了地球物理信息的解译工作,以期降低地球物理信息多解性对三维地质建模干扰,提高三维地质建模的准确性。最后,结合对成因模式与地球物理勘探标准的深入认识,文章实现了蒙阴金刚石深部含矿性分析,并初步圈定了成矿远景区。

基于CiteSpace的地质大数据与人工智能研究热点及前沿分析

为研究地质学领域的大数据和人工智能研究现状、热点和前沿,在中国知网(CNKI)核心期刊和Web of Science(WoS)核心数据库收集了2000—2022年相关中文文献3600篇、英文文献1803篇,利用社区结构分析软件CiteSpace,从合作作者、研究国家、研究机构、关键词聚类、关键词时空分布图谱等进行可视化分析,并统计了2021—2022年间,地质学领域国际顶级期刊(综合影响因子10以上)的文献进行前沿分析。分析结果表明,近10年内该研究领域全球累计发文量激增,以中国为代表的亚洲国家和以美国为代表的欧美国家研究为主,双方累计发文量相差不大,论文中介中心性欧美国家普遍较高。我国研究机构之间的交流合作居多,与国外的研究机构交流合作较少,国外研究机构则与之相反。该领域以应用机器学习类方法、知识图谱构建等,在地质灾害防治、地震解释、石油与天然气勘查、固体矿产资源预测等方向进行的科学研究为研究热点,以深度学习、集成学习、智能平台搭建等为手段的地球演化过程中的重大地质事件研究、全球性气候变化、极地及海洋地质研究、数字地质建模及定量分析、地震预报、地灾易发性精准评估等为研究前沿。

基于大数据关联规则算法的卡林型金矿床元素富集规律及找矿方法研究

大数据时代的到来,为卡林型金矿床开拓了新的找矿思路。本研究应用关联规则算法,挖掘滇黔桂“金三角”卡林型金矿床内微量元素与金矿化海量数据之间的关联性,提取元素异常组合,分析控制因素,定量构建找矿标志。结果显示矿床内元素异常组合分为4组:(1)强正关联显著富集元素(As、Sb、Hg、Tl、Ag、W和Rb),显示硫化和黏土化作用;(2)较强正关联略富集元素I(Zr、Th、Ta、Nb和Hf)和强负关联强迁出元素(Li和Sr),显示去碳酸盐化作用;(3)较强正关联略富集元素II(Sn、Zn、Ni、V、Co和Cu),显示硫化作用;(4)弱关联基本无富集元素(Cd、Pb、Ba、Bi、U和Mo),与成矿无显著关联。从大数据角度获取的元素异常组合,与学界关于Au主要在去碳酸盐化、硫化和黏土化条件下形成的认识一致。通过关联规则算法分别对与硫化和去碳酸盐化相关的元素建立定量找矿标志。硫化找矿标志:样品中As、Hg、Sb、Tl、W、Ag和Rb等元素内中高含量项数≥1、≥2、≥3、≥4和≥5时,对应的Au矿化分别为≥4.5×10^(-9)、≥47.0×10^(-9)、≥150×10^(-9)、≥500×10^(-9)和≥1000×10^(-9);样品内高含量项数≥1、≥2和≥3时,对应的Au矿化分别为≥150×10^(-9)、≥500×10^(-9)和≥1000×10^(-9);找矿过程中两组指标配合使用,确保不漏矿,高效圈矿。去碳酸盐化找矿标志:样品中Zr、Th、Ta、Nb和Hf含量任意两项出现正异常,认为样品经历过去碳酸盐化作用。定量识别的硫化和去碳酸盐化找矿标志可望在卡林型金矿找矿预测中发挥重要作用。本研究基于关联规则算法分析矿床元素富集规律、控制因素和定量构建找矿标志的方法,也可为其他类型矿床开展类似研究提供新思路。

面向地质封存及其泄漏风险评价的CO_(2)物联网在线监测

CO_(2)地质封存可以在控制CO_(2)排放的同时兼顾社会经济发展,已成为实现双碳战略目标必不可少的技术途径。然而,在CO_(2)地质封存过程中,由于封存的CO_(2)存在向封存区外泄漏或渗漏的风险,这可能对环境以及周围生物造成严重影响。因此,CO_(2)地质封存的安全性和有效性风险需要得到持续的监测,对于潜在泄漏的监测与识别是碳封存系统安全保障的关键环节。物联网在线监测技术具有的大范围、连续监测和智能分析等特征,较好地契合了地质封存场地的监测需求,但目前还没有在封存监测领域进行大规模应用。为了建立面向地质封存场地的物联网在线监测系统,首先介绍了传感器选择和传感器节点部署的依据,并提出了底层传感技术的设计思路,展示了已完成的碳排放在线监测系统。在传感器选择方面,采用红外CO_(2)传感器为主、激光CO_(2)传感器为辅、FT-IR巡逻监测的设计思路。在传感器节点部署方面,采用随机部署与固定部署结合,可移动部署实时优化的设计思路。在网络拓扑结构设计方面,在高泄漏风险的重点区域采用簇状拓扑和网状拓扑混合的方式,在边缘区域则采用星状拓扑、树状拓扑结构与主体区域相连的设计思路。未来伴随着传感器批量化和微型化生产的不断推进,传感器节点的分布组网将逐渐形成规模,物联网在线监测技术将在CO_(2)地质封存场地的监测领域发挥更为重要的作用。

多层次矿床本体的构建及在知识图谱中的应用

将具有多元异构性和复杂语义的矿床数据转化为结构化数据,是目前矿产资源勘查大数据领域面临的关键问题。传统的机器学习方法无法精确描述实体概念、属性及其属性值的语义信息,导致多源异构数据的可解释性较差。因此,可解释性的知识图谱已成为当前研究的热点。然而,当前矿床领域本体构建研究仍相对匮乏,这阻碍了矿床知识图谱的研究。本文聚焦于矿床领域的概念、关系、属性描述,结合知识工程、叙词表、复用前人本体及专家知识,采用基于知识工程和基于顶层本体相结合的本体构建方法,使用本体开发工具Protégé构建了以时空矿床文本为基础的矿床领域本体库,实现了矿床知识概念、关系的系统化、规范化、形式化表达。然后运用Neo4j构建本体库知识图谱,并以庞西垌多金属矿床为案例,将矿床本体与矿床数据进行了知识图谱连接,展示了矿床本体作为知识图谱骨架的重要性。本文研究对下一步矿床知识图谱推理分析具有一定的指导意义。

中国石油精细油藏描述技术新进展与展望

精细油藏描述研究是油田开发基础工作之一,其研究精度的不断提高,极大地促进了油田开发水平的提升。“十二五”和“十三五”期间,中国石油精细油藏描述研究针对多层砂岩油藏、复杂断块油藏、低渗透砂岩油藏、砾岩油藏、稠油油藏、特殊岩性油藏分别开展了技术攻关,形成了不同类型油藏配套技术系列。为了支撑剩余经济可采储量(SEC)储采平衡、老油田“压舱石”、采收率再提高、页岩油革命等“四大工程”,系统梳理中国石油精细油藏描述研究在5个方面的最新进展,分别是井震结合构造精细解释技术实现低级序断层定量描述,基于储层构型研究的单砂体刻画技术实现米级砂体描述预测,室内实验、数字岩心、物理模拟和数值模拟等方法技术实现综合剩余油表征,精细油藏描述成果平台建设实现研究过程协同共享和成果规范化存储管理,建章立制制定系列标准规范实现精细油藏描述研究规范化。通过系统总结精细油藏描述存在的8个方面问题,提出精细油藏描述的发展方向。

地质调查业务管理数据集成辅助决策系统架构与关键技术

在地质调查工作过程中,各类信息系统产生了海量的业务管理数据,需要解决这些多来源、高动态、复杂异构数据的有效集成辅助决策问题,驱动管理决策现代化。基于大数据、GIS、数据挖掘等技术,构建了地质调查业务管理数据集成辅助决策的总体架构;通过多源异构数据自动化动态集成处理、基于Hadoop的“湖仓一体”混合式数据组织管理、数据挖掘融合地理智能的分析决策模型等关键技术方法,研发了国家地质调查业务管理大数据系统。系统已接入24个数据源,实现了自动化动态集成,完成了1.5亿余条、20多万档异构数据的一体化组织管理,通过数据和分析服务有效辅助了管理决策。可有效解决大数据环境下数据集成辅助决策问题,提升国家地质调查工作管理决策的效率和水平。

基于BERTwwm与数据增强的地质实体识别研究

地质命名实体识别是识别地质文本中的地质实体并分类到准确的地质概念中的一项地质知识智能抽取任务,也是构建地质领域知识图谱的关键技术之一。本研究针对地质命名实体识别领域中复杂实体识别精度不足和样本标注成本较高这两大挑战,构建了一种地质实体识别模型BERTwwm—BiLSTM—Attention—CRF,该模型通过改进的预训练层BERTwwm并在模型中加入Self-Attention模块,显著提升了复杂地质实体的识别精度,对地质实体识别的精度达到92.67%的精确率,94.21%的召回率,以及93.29%的F1值。同时,为降低标注成本,提升小规模数据集的识别精度,本研究优化了模型构建流程,采用模型辅助标注方法,加快数据集的标注速度;改进简单数据增强方法,并利用地质字典有效扩充数据集,降低了人工标注的难度。经过实验证明,本研究提出的改进方法提高了地质实体识别效果,为地质文本分析提供了一种高效且经济的解决方案,有助于推动地质领域知识图谱的构建和地质信息的智能化处理。

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

大数据找矿信息平台建设及其在新一轮找矿突破战略行动中的应用

充分利用信息化手段支撑地质工作是地学研究的前沿领域.随着新一轮找矿突破战略行动的逐步展开,大数据信息技术对于开展地质找矿工作、构建地质信息数据库有重要意义.本文探讨了大数据在地质找矿行动中的作用与优势,并总结归纳了数据平台建设研究方法.大数据找矿信息平台的建设将为进一步找矿工作提供科学依据.借助大数据、云计算等现代信息技术为新一轮找矿突破战略行动赋能,这为找矿工作数据库体系的智能化建设提供了全新的思路.

管窥人工智能与大数据地球科学研究新进展

本期是《地学前缘》组织出版的“人工智能与大数据地质”主题专辑。它由17篇学术论文组成,涵盖了知识图谱、基于深度学习的图像识别、非结构化地质信息的机器可读表达、图形大数据与社区发现、关联规则算法、三维地质模拟与成矿预测、物联网与在线监测系统等不同主题,提供了极其有价值的应用场景和研究案例,在一定程度上反映了中国人工智能与大数据地球科学领域研究的最新进展,值得同行关注。

地质大数据资产化管理探索与实践

地质大数据是国家重要的战略资源,地质数据是社会经济活动重要的组成要素,具有资产属性,是国有资产的重要组成部分。对地质大数据进行资产化管理,是规范地质大数据管理、发挥地质大数据经济价值、保证国有资产保值增值的必然途径。系统研究了美国国家地理空间数据资产组合管理实践和国内企业数据资产化管理取得的成果,分析了中国地质调查局地质大数据资产管理现状和存在的问题,勾画出地质大数据资产化管理策略,明确了地质大数据资产化管理的对象和主要要素,初步构建了数据价值评估体系,提出了地质大数据资产化管理的方式和加强数据资产产权保护的建议,并介绍了中国地质调查局已开展的地质大数据资产化管理实践和数据资产登记工作。