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。     

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000

The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.     

免共振沉桩过程对地表振动影响的FLAC3D数值模拟

为研究免共振沉桩过程对地表振动影响,采用密度放大法以消除模型桩弹性模量过大对计算效率的影响,在有限差分软件FLAC^3D中建立了相应的连续振动沉桩模型,并和文献中的现场测试结果进行了比较,分析了激振力幅值和振动频率这两个施工参数对地表振动响应的影响。结果表明：密度放大法可有效提高数值模拟的计算效率,模拟沉桩7.0倍桩径（4.9 m）所需计算时间约为12.0 h,数值结果较好地模拟了现场测试中免共振沉桩的地表振动影响;激振力幅值和振动频率均主要对近场（水平距离为5.0倍桩径范围内）的地表振动有明显影响;临界沉桩深度与地表振动影响峰值相对应,该深度随水平距离先增大后趋于稳定;激振力幅值对临界沉桩深度的改变不明显,振动频率对远场临界沉桩深度则有较明显影响。     

普朗斑岩型铜矿床成矿元素多重分形特征及其矿化强度指示

成矿元素品位的变化受控于长期地质作用过程,具有较强的非线性特征,定量刻画其变化特征有助于深入理解成矿过程,并为找矿预测提供理论依据。运用多重分形去趋势移动均值（MFDMA） 法,分析云南普朗斑岩型铜矿床4号勘探线钻孔Cu元素品位的多重分形特征及其对矿化强度的指示意义。结果显示：所有钻孔Cu元素品位具有多重分形特征,且不同矿化等级的元素分布存在局部奇异性差异,多重分形强度随矿化强度等级减弱而增加;这些特征指示在钾硅化带—带内侧强矿化钻孔的高品位相对聚集,而带外侧角岩化—青磐岩化带的高品位相对分散;通过数据随机重排技术推测Cu品位多重分形结构奇异性差异是由其分布概率密度和长相关性共同作用引起,后者的作用贡献大于前者。     

DYYZ-Ⅱ型自动气象站测温故障排除方法与思路

文章通过DYYZ-Ⅱ型自动气象站出现的一次气温、地温数据缺测故障,分析了DYYZ-Ⅱ型自动气象站出现测温故障时的故障排除过程与排除思路,为保障技术人员在处理此类故障时提供了方法和依据。     

中国城市标度律及标度因子时空特征

城市标度律刻画了城市系统中城市要素与人口数量之间的规模缩放关系,但标度律在中国城市系统中的适用性和意义尚不明确。本文采用中国275个地级及以上城市数据,探究不同城市要素与人口规模的标度关系,并从时空维度探究标度因子的时空演化规律。结果表明：作为典型的快速城市化发展中国家,中国城市发展符合标度律,且呈现出与发达国家不同的特征：中国城市人口集聚带来的经济产出集聚效应显著,而土地利用、基础设施与公共服务的规模经济效应还有待增强。部分基础设施、生活设施要素与人口规模呈超线性或线性关系,与预期的次线性相悖,基础教育、医疗等要素的标度因子则远低于发达国家的次线性阈值,反映了公共资源向大城市聚集、不同规模城市发展不平衡等快速城镇化进程中城市系统的独特性。标度因子的区域分异反映城市发展协调程度差异,其中东北地区不同规模城市差异最为显著。主要城市要素标度因子随时间的变化表明,中国大城市的经济产出集聚效应在2009年最为显著,而后略有下降;中国大城市土地扩张总体上自2000年以来持续加速,2008年以后仍维持在较高水平,管控城市特别是大城市的外延式扩张、提高土地利用效率具有紧迫性。中国城市标度律研究有助于认知快速城市化地区城市系统特征和演化规律,为实现区域协调发展与落实新型城镇化发展战略提供支持。     

中尺度自动站数据处理系统

为了解决中尺度自动气象站建设后探测数据的应用和共享问题,利用数据库等计算机技术,开发一套自动数据处理系统。实现了数据的处理储存、灾害天气预警和数据在不同部门之间共享。