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.     

塔里木盆地满加尔凹陷西缘奥陶系柯坪塔格组下段海相砂岩准层序成因与意义

通过精细岩芯描述、单井沉积微相分析与地震相、测井相综合研究,认为在塔里木盆地满加尔凹陷西缘奥陶系柯坪塔格组下段顶部含油气砂体中,发育3种类型的准层序：①粒级由底部粗—中下部细—上部粗的、反映整体向上变浅的海侵滞留沉积—海泛泥岩—滨岸沉积准层序,羊屋2井5460~5468 m含油气段位于该准层序内;②粒级向上变粗的滨外泥岩—临滨－前滨沉积,顶部受风暴影响的准层序,跃南2井6503~6512 m含油气段位于该准层序内;③粒级向上变细的潮下带—潮间带沉积的准层序,吉南1井5465~5486 m含油气段属于此准层序。发育于羊屋2井中的第一种准层序内含油气砂体在时间上属于较早的准层序3,发育于跃南2井、哈得13井中的第二种准层序与吉南1井中的第三种准层序内的含油气砂体属于稍晚的准层序4。早期沉积的、发育于羊屋2井顶部的第一种准层序是有利储层发育段,该井区以北靠近柯坪塔格组下段顶面尖灭线附近的准层序3时期沉积的临滨相储层是油气聚集成藏的有利部位。     

湖南石门杨家坪下寒武统杷榔组三段混合沉积研究

湖南石门杨家坪下寒武统杷榔组三段发育陆源碎屑与海相碳酸盐的混合沉积。综合前人研究成果,通过对研究区野外露头资料的详细观测和室内样品分析及数据统计等工作,对研究区杷榔组三段混合沉积进行了详细的研究。研究区混合沉积特征宏观上表现为由陆源碎屑与混积物交互沉积、碳酸盐与混积物交互沉积和不同类型混积物本身的交互沉积构成的混积层系,微观上表现为陆源碎屑组分与碳酸盐组分混合沉积所形成混积岩;混合沉积类型主要为内潮汐沉积作用和浊流沉积作用形成的复合式混合沉积Ⅱ,次为深水原地沉积作用形成的渐变式混合沉积,但是不同成因的沉积作用形成的混合沉积类型往往叠加在一起,形成复合式混合沉积Ⅱ+渐变式混合沉积的混合沉积复合体;混合沉积环境为深水斜坡,发育内波、内潮汐混合沉积微相、深水原地混合沉积微相和远源浊流混合沉积微相等类型;在沉积环境分析的基础上,根据内波、内潮汐沉积特征,剖析了混合沉积的沉积机理;结合混合沉积的成因、沉积机理、产出部位及沉积环境等因素,以沉积学原理为指导,建立了研究区杷榔组三段的混合沉积模式。