黔北正安地区五峰组—龙马溪组页岩气地质特征及有利区预测     

贺永忠  向坤鹏  安亚运  易成兴  杨忠琴  于宁 《中国地质调查》2020,7(3):21-29

为明确黔北正安地区上奥陶统五峰组至下志留统龙马溪组的页岩气地质特征,充分应用页岩气地质调查、重磁电、二维地震、地质调查井及测试分析等工作方法,开展黔北正安地区1:5万页岩气基础地质调查,对五峰组至龙马溪组富有机质页岩的分布、沉积环境、有机地球化学、岩石矿物、储集性能及含气性等特征进行分析研究,结果表明: 研究区五峰组—龙马溪组富有机质页岩为深水陆棚相沉积,主要分布有安场向斜、斑竹向斜和泥高向斜,厚度14.5~55 m,埋深0~3 200 m; 有机碳(TOC)含量1.0%~4.0%,有机质镜质体反射率(R_o)为1.82%~2.23%,有机质类型以Ⅰ型干酪根为主; 岩石主要由石英、长石和黏土矿物组成,脆性矿物含量高,一般大于50%; 孔隙度为2.03%~3.89%,渗透率为0.35×10^-5~1.86×10^-5μm²,表现为低孔、特低渗的特征; 最高含气量为2.88 m³/t,显示出较好的含气性特征。综合分析和评价圈出3个页岩气聚集有利区,分别为安场区块、斑竹区块和泥高区块,这为研究区页岩气进一步勘探开发指明了方向。  相似文献   

基于深度残差网络的机载LiDAR点云分类     

赵传  郭海涛  卢俊  余东行  张保明 《测绘学报》2020,49(2):202-213

机载LiDAR点云的分类是利用其进行城市场景三维重建的关键步骤之一。为充分利用现有的图像领域性能较好的深度学习网络模型,提高点云分类精度,并降低训练时间和对训练样本数量的要求,本文提出一种基于深度残差网络的机载LiDAR点云分类方法。首先提取归一化高程、表面变化率、强度和归一化植被指数4种具有较高区分度的点云低层次特征;然后通过设置不同的邻域大小和视角,利用所提出的点云特征图生成策略,得到多尺度和多视角点云特征图;再将点云特征图输入到预训练的深度残差网络,提取多尺度和多视角深层次特征;最后构建并训练神经网络分类器,利用训练的模型对待分类点云进行预测,经后处理得到分类结果。利用ISPRS三维语义标记竞赛的公开标准数据集进行试验,结果表明,本文方法可有效区分建筑物、地面、车辆等8类地物,分类结果的总体精度为87.1%,可为城市场景三维重建提供可靠的信息。  相似文献   

基于点云滤波的DOM自动生产算法     

白世晗  刘斌  白少云  顾世祥  邢宽平 《测绘通报》2020,(2):82

针对目前生产高精度DOM生成效率低、人工编辑干预较大等现状，提出了一种基于点云滤波的DOM自动生产方式。该方法通过对空三解算后的影像匹配密集点云DSM，使用基于TIN的加密滤波方式进行滤波计算，解决了滤波后空洞区域插值问题，制作出满足微分纠正需求的DEM模型，并基于该模型进行微分纠正生成DOM成果。通过选取ISPRS城区试验数据和实际工程山区数据进行验证，证明该算法不仅提高了效率，同时优化了成果质量，DOM成果精度符合规范要求。  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   

测绘地理信息档案管理对策研究     

李明 《测绘与空间地理信息》2020,(3):9-11,15

国民经济建设对测绘地理信息档案的需求旺盛,如何对测绘地理信息数据进行归档管理,提高测绘地理信息档案存储的安全性及方便再利用,已经成为测绘地理信息档案管理部门需要研究的重要课题。本文通过分析当前测绘地理信息档案管理的现状,指出存在的问题,提出有力的改进措施。  相似文献   

MEEMD-Elman神经网络的电离层TEC预报模型     

汤俊  高鑫 《大地测量与地球动力学》2020,40(4):395-399

利用IGS中心提供的不同纬度的电离层TEC值,建立基于改进的集总平均经验模态分解(MEEMD)算法和Elman回归神经网络(ERNN)模型相结合的电离层TEC预报模型。实验结果表明,在低、中、高不同纬度采用本文方法预报5 d电离层TEC的预测值的均方根误差最优可达到0.96 TECu,相对精度最优达到95.4%,精度较EMD-ERNN模型及单一ERNN模型有显著提高。  相似文献   

Deep gold mineralization features of Jiaojia metallogenic belt,Jiaodong gold Province: Based on the breakthrough of 3000 m exploration drilling     

《China Geology》2020,3(3):385-401

Recently, continuous breakthroughs have been made about deep gold prospecting in the Jiaodong gold province area of China. Approximately 5000 t of cumulative gold resources have been explored in Jiaodong, which has thus become an internationally noteworthy gold ore cluster. The gold exploration depth has been increased to about 2000 m from the previous <1000 m. To further explore the mineralization potential of the Jiaodong area at a depth of about 3000 m, the Shandong Institute of Geological Sciences has drilled an exploratory drillhole named “Deep drillhole ZK01” to a depth of 3266 m. Hence, as reported herein, the mineralization characteristics of the Jiaojia metallogenic belt have been successfully documented. ZK01 is, to date, the deepest borehole with an gold intersect in China, and constitutes a significant advance in deep gold prospecting in China. The findings of this study further indicate that the depth interval of 2000 m to 4000 m below the ground surface in the Wuyi Village area incorporates 912 t of inferred gold resources, while the depth interval of 2000 m to 4000 m below the surface across the Jiaodong area possesses about 4000 t of inferred gold resources. The Jiaojia Fault Belt tends to gently dip downward, having dip angles of about 25° and about 20° at vertical depths of 2000 m and 2850 m, respectively. The deep part of the Jiaojia metallogenic belt differs from the shallow and moderately deep parts about fracturing, alteration, mineralization, and tectonic type. The deep zones can generally be categorized from inside outward as cataclastic granite, granitic cataclasite, weakly beresitized granitic cataclasite, beresitized cataclasite, and gouge. These zones exhibit a gradual transitional relation or occur alternately and repeatedly. The mineralization degree of the pyritized cataclastic granite-type ore in the deep part of the Jiaojia metallogenic belt is closely related to the degree of pyrite vein development; that is, the higher the pyrite content, the wider the veins and the higher the gold grade. Compared to the shallow gold ores, the deep-seated gold ores have higher fineness and contain joseite, tetradymite, and native bismuth, suggesting that the deep gold mineralization temperature is higher and that mantle-sourced material may have contributed to this mineralization. ZK01 has also revealed that the deep-seated ore bodies in the Jiaojia metallogenic belt are principally situated above the main fracture plane (gouge) and hosted within the Linglong Granite, contradicting previous findings indicating that the moderately shallow gold ore bodies are usually hosted in the contact zone between the Linglong Granite and Jiaodong Group or meta-gabbro. These new discoveries are particularly significant because they can help correct mineralization prospecting models, determine favorable positions for deep prospecting, and improve metallogenic prediction and resource potential evaluation.  相似文献   

一种基于M估计的Robust-ELM滑坡位移预测方法     

高彩云  高宁 《大地测量与地球动力学》2019,39(2):153-157

采用传统ELM算法进行滑坡位移预测时，其网络输出权值由最小二乘估计得出，导致ELM抗差能力较差，从而造成网络训练参数不准确。为此，将M估计与ELM相结合，提出一种基于M估计的Robust-ELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值，以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例，将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明，该方法能够有效降低粗差对预测的影响，具有良好的抗差能力。  相似文献   

基于GPT2w模型化加权平均温度反演可降水量     

翟树峰  吕志平  李林阳  吕浩  邝英才  王方超 《大地测量与地球动力学》2019,39(7):733-737

提出一种基于GPT2w模型化加权平均温度反演大气可降水量的方法，并分析附加系统偏差改正的模型化加权平均温度对可降水量的影响。结果表明，基于GPT2w模型化加权平均温度反演的大气可降水量的精度与基于Bevis公式计算的加权平均温度反演的大气可降水量的精度相当；对GPT2w模型化加权平均温度进行系统偏差改正后，大气可降水量的精度有一定改善，但改善率不到1%。  相似文献   

It’s Time To Learn About Learning: Where Should the Environmental and Natural Resource Governance Field Go Next?     

Andrea K. Gerlak  Tanya Heikkila  Sharon L. Smolinski  Derek Armitage  Dave Huitema  Brendan Moore 《社会与自然资源》2019,32(9):1056-1064

This Commentary reflects on the state of the scholarship on learning for environmental and natural resource policy and governance. How have we been learning about learning? We highlight theoretical and empirical advancements related to learning, as well as areas of divergence between learning theories and frameworks, and underdeveloped knowledge around processes and outcomes. To address these limitations and improve progress in both theory and practice, we offer recommendations for learning scholarship by focusing on how to collectively engage in ‘learning about learning’.  相似文献