A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

ECOLOGICAL SERIES OF SOIL ANIMALS IN DARLIDAI MOUNTAIN

The ecological series of soil animals under the broad-leaved and pine mixed forest in Darlidai Mountainwas studied. Seven sample plots were selected according to different altitude gradients, which belong to different vegeta-tion types. By investigating and analyzing soil animals in every sample plot it is found that there are 45 groups and 1956individuals, which axe involved in 3 phylums, 7 classes, 16 orders, respectively. The altitude is a key factor which af-fects ecological series of soil animals. Both the groups and individuals of soil animals increase with altitude increasingunder certain conditions, which contrastes with ordinary cases, resulting from special micro-climate in studied area. Thegroups and individuls of soil animals are the most under the broad-leaved and pine forest on the top of the mountain, andthe least under Picea-Abies forest in the foot of the mountain.     

富氏谱分析法模拟地壳垂直运动的地学依据及其策应力模型

应用地壳波浪与镶嵌构造学说对富氏谱分析法提取地壳垂直形变信息的科学性做了地质学意义上的阐释 ,并提出了根据多期形变资料提取特定波段上构造策应力的数学模型     

Optical Flash of GRB 990123: Constraints on the Physical Parameters of the Reverse Shock

The optical flash accompanying GRB 990123 is believed to be powered by the reverse shock of a thin shell. With the best-fit physical parameters for GRB 990123 and the assumption that the parameters in the optical flash are the same as in the afterglow, we show that: 1) the shell is thick rather than thin, and we have provided the light curve for the thick shell case which coincides with the observation; 2) the theoretical peak flux of the optical flash accounts for only 3×10~-4 of the observed. In order to remove this discrepancy, the physical parameters, the electron energy and magnetic ratios, εe and εB, should be 0.61 and 0.39, which are very different from their values for the late afterglow.     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.     

复杂模型重力位场快速正反演方法及应用

提要 本文详细讨论了一种三维重力位场快速正反演方法。作者在前人工作的基础上,对算法作了行之有效的改进,通过对反演中的不稳定因素进行各种理论模型试算,得出保证迭代反演稳定收敛的准则,编制出可在微型机IBM—PC上运行的人机对话式自动正反演程序。本文还对各种不均质模型进行了模似计算并将该方法应用于某含油气沉积盆地的双层界面构造研究,揭示出了储油有利地段。     

Cosmological implications of BL Lac objects

It has become clear in recent years that relativistic beaming is a good explanation for the BL Lac phenomenon. Of studies based on the relativistic beaming model of BL Lac objects, we note that the orientation of jet's axis to the line-of-sight is very small and, therefore, the observed flux emitted from a rapidly moving source is orders of magnitude higher than the flux in its rest-frame:F _obs = ^{3 +} F _intr, where is the bulk relativistic Doppler factor. Then the observed apparent magnitudem _v must be corrected for this effect. For our 39 samples, the corrected apparent magnitudem _v ^corr and logZ have a good correlation.     

泸州古隆起对贵州赤水地区早、中三叠世沉积环境和相带展布的控制

地质历史中,区域大地构造和区域古地貌单元的不同特征是控制沉积相带展布及发育的主要因素之一。东吴运动形成的泸州古隆起是影响本区沉积相带展布的重要因素。早、中三叠世时,泸州古隆起的高部位向南延伸到太和场以南,宝元以北一带。宝元、龙爪等构造处于泸州古隆起东南部斜坡上,而宝元构造处于古隆起东南部斜坡上部,龙爪构造处于古隆起东南部斜坡中部。此古地貌格局,与区域海平面升降一起,控制了早三叠世沉积相带展布和地层厚度。在古地貌高的隆起区域,由于水体开阔、水动力条件相对较强,沉积了有利于形成储集体的滩相沉积物（生屑滩、砂屑滩、鲕粒滩等）。从泸州古隆起的高部位向古隆起的斜坡区,地层厚度逐渐增大,沉积物向粒度变细、颜色加深的特征相变。     

中国大别山东南缘首次发现大坝陨坑构造

简介首次在大别山东南缘安徽省境内发现的大坝陨坑构造。经初步评价,知陨坑呈椭圆形,长轴呈北北东向,长约19km,短轴长约12km,最大坑深约2km,是一个有中央隆起区的复杂型陨坑。在卫片上陨坑显示环形影像,地貌形态为一洼地。陨坑基座保存尚好,可对它直观和追索陨坑构造边界。形成于230Ma左右的各类撞击变质岩石系列齐全,其中含有柯石英等典型的撞击变质矿物及撞击碎理等超微构造,特别是在陨坑基座内壁普遍发现有鉴别陨坑构造最可靠的标志——干裂自角砾岩,都证明大坝环形影像是一个典型的陨星撞击坑构造。它的发现,具有很大的科学及经济意义,对今后褶皱山区寻找和研究陨坑构造具有示范和指导作用,同时大大丰富了建设大别山世界地质公园的地质依据。无庸置疑此发现将促进大别山旅游业的发展及陨星撞击科普知识的传播。     

大沽河下游咸水入侵区含水砂层水敏感性评价

通过室内砂柱的水平渗流试验,对大沽河下游成水入侵区含水砂层的水敏感性进行了评价。对海水入侵治理的注淡压成和抽成方案过程中含水层的渗透性变化进行了研究．提出了控制和减小方案实施过程中含水砂层渗透性降低的方法和措施。研究结果表明：该含水层具有显著的水敏感性特征,注淡压成过程导致其渗透性下降幅度超过了两个数量级;注淡和抽成的流速分别超过21ml／min和42ml／min时,亦能引起含水砂层水敏感性的发生,称为临界流速;淡水驱替咸水过程中存在一个临界离子强度-7638us／cm,驱替水的离子强度不宜小于该值以避免水敏性的发生。