GPS网络RTK的质量控制

随着科技的发展与工程应用的深入,传统RTK技术的不足也逐渐体现出来。网络RTK的出现克服了它的局限性。本文简要介绍了网络RTK的概念、工作原理、误差来源与平差、质量控制方法。     

Automatic equipment to monitor groundwater radon

Prototype instrumentation, able to automatically measure groundwater radon content variations, is presented. The equipment is made of stainless steel and has spherical valves with automatic and pneumatic control. The deemanation of the gases from the water is obtained by evacuating a suitable expansion chamber. The instrumentation can make discrete sampling ranging from 1 per hour to 1 per 99 hours. The equipment was tested in the laboratory: the efficiency was measured by means of a²⁶⁶Ra solution. A mean value of (0.65±0.07) count/s/Bq was obtained. A calibration test was carried out by comparing countings from the automatic equipment with those obtained by the standard laboratory cell. Results of an operational check over a period of approximately one year indicate that variations in radon at the calibration site are attributable more to meteorological than to tectonic causes.     

DAS24-3B数据采集系统采集控制模块的工作原理

DAS24-3B是中国科学院地质与地球物理研究所研制的宽频带地震数据采集系统。采集控制模块是该系统的重要组成部分。本文阐述了控制模块的工作原理，介绍了DAS24-3B系统的基本组成和采集器工作过程，并给出该系统如何与笔记本端应用程序界面通讯和联络的操作建议，使该系统能够在工作中发挥更大的作用。     

基于GPRS的远程气象观测数据实时采集传输系统及其应用

通过对远程自动气象站数据实时采集传输通讯方式的比较，提出基于通用分组无线业务（GPRS）的远程自动气象站数据实时采集和传输系统的技术方案，以小数据流方式，实现野外自动气象站观测数据的实时采集、传输和对观测设备运行的监控，达到节约经费，提高数据传输时效的目的，并将此系统成功地用于苏通长江公路大桥施工期气象环境监测与预警系统中。     

GIS技术支持下的江苏省环境自动监控系统建设

GIS是江苏省环境自动监控系统建设中重要的支撑性信息技术，在GIS数据以及软件系统的支持下．全省各种环境自动监控信息的获取、存贮、管理和显示实现了信息处理业务化集成，网络化环境自动监控信息共享与服务GIS系统为增强我省适应环境管理现代化的环境信息快速获取和应急决策响应能力发挥了良好作用。     

自动气象观测数据的质量控制

精细化气候变化分析及气候预测服务亟待发展,对高分辨率优质量的气候资料需求不断增长。本文利用缺测率、对比差值、空间一致性、粗差率、一致率5个指标对2009—2014年浙江省2 209个自动站温度数据进行评估分析,运用开放式可调整的评分标准,选取数据质量稳定可靠的站点,满足气候研究与业务的发展需求。共取得91个一级可用站,499个二级可用站,363个三级可用站,一级站主要分布于宁波和嘉兴地区,二级和三级站在各地都有较均匀的分布。在全省气候统计分析中,可以根据需求利用可用站。此外,有可用自动站参与的温度分布图完美呈现了地形对温度的影响,也使城市化发展突显出来,精细化的气候分析能更有效的为高山地区耕种制度及城市建设与规划提供科学依据。     

Unsupervised regionalization of the United States into landscape pattern types

We present a pattern-based regionalization of the conterminous US – a partitioning of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of the land surface into a number of landscape pattern types (LPTs) – spatial units each containing a unique quasi-stationary pattern of land cover classes. To achieve this goal, we use a recently developed method which utilizes machine vision techniques. First, the entire National Land Cover Dataset (NLCD) is partitioned into a grid of square-size blocks of cells, called motifels. The size of a motifel defines the spatial scale of a local landscape. The land cover classes of cells within a motifel form a local landscape pattern which is mathematically represented by a histogram of co-occurrence features. Using the Jensen–Shannon divergence as a dissimilarity function between patterns we group the motifels into several LPTs. The grouping procedure consists of two phases. First, the grid of motifels is partitioned spatially using a region-growing segmentation algorithm. Then, the resulting segments of this grid, each represented by its medoid, are clustered using a hierarchical algorithm with Ward’s linkage. The broad-extent maps of progressively more generalized LPTs resulting from this procedure are shown and discussed. Our delineated LPTs agree well with the perceptual patterns seen in the NLCD map.     

High-reliability zircon separation for hunting the oldest material on Earth: An automatic zircon separator with image-processing/microtweezers-manipulating system and double-step dating

Despite the recent development in radiometric dating of numerous zircons by LA-ICPMS, mineral separation still remains a major obstacle, particularly in the search for the oldest material on Earth. To improve the efficiency in zircon separation by an order of magnitude, we have designed/developed a new machine-an automatic zircon separator(AZS). This is designed particularly for automatic pick-up of100 μm-sized zircon grains out of a heavy mineral fraction after conventional separation procedures. The AZS operates in three modes:(1) image processing to choose targeted individual zircon grains out of all heavy minerals spread on a tray,(2) automatic capturing of the individual zircon grains with microtweezers, and(3) placing them one-by-one in a coordinated alignment on a receiving tray. The automatic capturing was designed/created for continuous mineral selecting without human presence for many hours. This software also enables the registration of each separated zircon grain for dating, by recording digital photo-image, optical(color) indices, and coordinates on a receiving tray. We developed two new approaches for the dating; i.e.(1) direct dating of zircons selected by LA-ICPMS without conventional resin-mounting/polishing,(2) high speed U-Pb dating, combined with conventional sample preparation procedures using the new equipment with multiple-ion counting detectors(LA-MIC-ICPMS).With the first approach, Pb-Pb ages obtained from the surface of a mineral were crosschecked with the interior of the same grain after resin-mounting/polishing. With the second approach, the amount of time required for dating one zircon grain is ca. 20 s, and a sample throughput of 150 grains per hour can be achieved with sufficient precision(ca. 0.5%).We tested the practical efficiency of the AZS, by analyzing an Archean Jack Hills conglomerate in Western Australia with the known oldest(4.3 Ga) zircon on Earth. Preliminary results are positive; we were able to obtain more than 194 zircons that are over 4.0 Ga out of ca. 3800 checked grains, and 9 grains were over 4300 Ma with the oldest at 4371 ± 7 Ma. This separation system by AZS, combined with the new approaches, guarantees much higher yield in the hunt for old zircons.     

基于岩石图像深度学习的岩性自动识别与分类方法

岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。