基于IUCN《矿山生物多样性管理系列指南》的大宝山矿生态修复实践研究     

罗明  张世文  魏洪斌  周鹏飞  周妍  陈妍  李灼超  张金桃 《地学前缘》2021,28(4):90-99

为研究矿山开采及修复过程中如何开展生物多样性管理,本文借鉴IUCN(世界自然保护联盟)发布的《矿山生物多样性管理系列指南》成果,将相关理论和方法应用到广东大宝山矿生态修复实践中。文章在介绍IUCN的“综合生物多样性管理体系”方法主要内容的基础上,将其作为大宝山矿实施生态修复依据,阐述了该管理体系在矿山生态修复工程中的应用,并结合广东大宝山矿的实际情况,提出了大宝山矿生物多样性管理的保护与修复框架,包括全周期的生物多样性管理手段,重点阐述大宝山矿生物多样性调查与监测、评价、保护与受损控制及恢复的技术方案,最后分析了对中国矿山生物多样性保护和修复的启示,IUCN矿山生物多样性管理系列指南对于指导我国矿山生态修复具有重要作用。  相似文献   

面向知识服务的地质资料管理转型研究     

李敏  傅洁  陈安蜀  李磊  王国明  曾乐 《地质与资源》2021,30(1):92-98

针对传统地质资料服务方式单一、数据共享困难等问题,提出了地质资料管理的转型方案.对面向知识服务的地质资料知识组织方式、知识检索以及知识管理理念如何指导地质资料服务等方面进行了探讨.以地质数据本体构建、基于关联数据技术的地质资料语义化组织以及基于地质大数据的知识检索模型搭建为转型手段,介绍了促进地质资料转型升级的语义化描述及数据关联技术,以期对地质资料管理转型起到参考作用.  相似文献   

对甘肃矿山生态保护修复资金管理的思考及建议          下载免费PDF全文

梁廷栋 《甘肃地质》2021,(4):94-97

矿山开采生产的生态保护与修复工作,涉及自然资源、生态环境、水利、林草等多部门,结合矿政管理工作实际,梳理了矿山生态保护修复资金类型,总结剖析了目前不同部门分头管理存在的问题,在此基础上,提出应整合各部门矿山生态保护修复方案及资金,形成管理合力,把生态保护修复工作贯穿到从地质勘查、矿山建设、开采、闭坑、管护的全过程,边开采边治理,整体保护,综合整治,系统修复。  相似文献   

一种基于机器学习算法的岩性填图方法     

冀全伟  王文磊  刘治博  祝茂强  袁长江 《地质力学学报》2021,27(3):339-349

通过野外地质调查与机器学习方法的有机融合,提出了一种基于梯度提升决策树算法的岩性单元填图方法。研究以多龙矿集区为模型试验区,选择1∶5万勘查地球化学数据为基础预测数据,以1∶5万区域地质图为参考,进行基于梯度提升决策树算法的岩性预测填图模型试验。首先选择研究区内小范围空白区开展野外填图,建立原始数据集并初步构建岩性单元与预测数据对应关系;其次利用机器学习方法对预测数据进行多分类任务,进而开展目标填图区预测填图工作;最后通过概率选区选定概率较低目标区,开展进一步的小范围野外地质调查填图,对原始数据和知识库进行补充,迭代循环以上流程,直至预测填图达到要求。试验显示,随着迭代次数的增加,模型精度不断提高,并在7次迭代后模型准确率达到87%。该方法强调在实际应用中野外地质调查与基于机器学习预测填图的深度融合,以及野外实地工作在整个流程中的重要性和不可或缺性;同时能够充分挖掘已有数据资料的有用信息,用于辅助修正已有岩性填图内容,或根据已勘探区资料对邻近的未勘探区进行岩性分类,有效减少野外填图工作量,是对岩性填图方法、地质单元定量预测识别的有益探索,为区域地质填图工作提供了新的参考思路和辅助手段。   相似文献   

全民所有自然资源资产管理考核评价机制与方法研究     

周玉  邹朝晖 《上海国土资源》2021,(1):90-94

本文采用对比分析与归纳分析法,在明确全民所有自然资源资产管理考核评价机制定位的基础上,充分借鉴和参考现有相关考核评价制度的设计思路,围绕“考核谁、谁来考、怎么考、考什么、结果怎么定、结果怎么用”等关键问题,从考核评价对象和实施主体、考核评价方式和实施、考核评价结果应用、考核评价内容和指标体系构建、考核评价结果评定方法等方面,提出构建全民所有自然资源资产管理考核评价机制和方法的思路,为推进生态文明建设和自然资源资产产权制度改革提供支撑。  相似文献   

迭代法建立的低频模型在储层反演中的应用     

王伟  李炳颖  黄鑫  陈易周 《中国地质调查》2021,8(1):125-133

储层预测的精度直接制约着油气开发的经济性与有效性,而低频信息的补偿是改善储层反演效果的有利途径之一。常规的测井曲线内插低通滤波建模方法难以精细表征复杂地质背景下的低频模型。为降低研究区特殊地质体对储层反演带来的影响,采用迭代法建立低频模型,建模中分步考虑压实作用、特殊岩性等因素影响,并通过反演效果不断迭代更新低频模型,最终准确建立反映不同岩性(包含火山岩)的反演低频模型。研究表明: 该方法能够保证储层反演结果的可靠性,为储层精细描述提供了资料; 同时基于反演结果,对东海西湖凹陷W气田主力层(P1)有利储层进行重新刻画,优化了开发井水平段的位置,该井实施结果证实钻遇了优质储层。用迭代法建立的低频模型为复杂地质背景地区储层预测提供了一种更有效的建模方法。  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   

一种基于微服务架构的突发事件预警辅助决策系统的设计与实现     

陈石定  刘翔  汪应琼 《南京气象学院学报》2019,11(5):609-620

单体式架构应用在突发事件预警辅助决策系统开发中不能满足现实需求,本文引入了微服务架构设计开发该辅助决策系统.通过分析微服务架构在复杂系统中相对于传统单体式架构的应用优势,设计出一种基于微服务架构的突发事件预警信息发布辅助决策系统,该系统选用Spring Cloud微服务框架,并对其进行适当的扩展,创建了基于该系统设计的注册中心与网关.系统采用二三维一体化地理信息系统作为展示平台,通过接入各行业静态、危险源动态监测数据,根据设定的模型进行数据融合、处理,辅助进行预警信息生成、发布及应急处置阶段的指挥决策.所设计方案在湖北省突发事件预警发布辅助决策系统中得以实际应用,验证了该类系统使用微服务架构的合理性和有效性.  相似文献   

Integrating farmers’ adaptive knowledge into flood management and adaptation policies in the Vietnamese Mekong Delta: A social learning perspective     

《Global Environmental Change》2019

Flood management and adaptation are important elements in sustaining farming production in the Vietnamese Mekong Delta (VMD). While over the past decades hydraulic development introduced by the central government has substantially benefited the rural economy, it has simultaneously caused multiple barriers to rural adaptation. We investigate the relational practices (i.e., learning interactions) taking place within and across the flood management and adaptation boundaries from the perspective of social learning. We explore whether and how adaptive knowledge (i.e., experimental and experiential knowledge) derived from farmers’ everyday adaptation practices contributes to local flood management and adaptation policies in the selected areas. We collected data through nine focus groups with farmers and thirty-three interviews with government officials, environmental scientists, and farmers. Qualitative analysis suggests that such processes are largely shaped by the institutional context where the boundary is embedded. This study found that while the highly bureaucratic operation of flood management creates constraints for feedback, the more informal arrangements set in place at the local level provide flexible platforms conducive to open communication, collaborative learning, and exchange of knowledge among the different actors. This study highlights the pivotal role of shadow systems that provide space for establishing and maintaining informal interactions and relationships between social actors (e.g., interactions between farmers and extension officials) in stimulating and influencing, from the bottom-up, the emergence of adaptive knowledge about flood management and adaptation in a local context.  相似文献