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.     

An approximate method for evaluating the shear band thickness in saturated soils

A formula for the thickness of a shear band formed in saturated soils under a simple shear or a combined stress state has been proposed. It is shown that the shear band thickness is dependent on the pore pressure properties of the material and the dilatancy rate, but is independent of the details of the combined stress state. This is in accordance with some separate experimental observations. Copyright © 2004 John Wiley & Sons, Ltd.     

Neutrino mass,luminosity variation,and spectrum of SN1987A

In this paper, the neutrino mass has been determined from SN1987a observation in a manner that the simultaneity of neutrino emission is not regarded as the starting point, but is itself defined through the analysis by Monte-Carlo simulation. The result is that the neutrino mass lies in 3–4 eV, possiblym _v 3.6 eV. Neutrino luminosity variation and neutrino spectrum are also obtained. Comparison with theories gives further support to the mass determination, and also predicts the mass of progenitor star of SN1987a to be in the range of 12–25M .The project supported by the National Natural Science Foundation of China.     

Possible chemical structures and biological precursors of different vitrinites in coal measure in Northwest China

Chemical structure of Jurassic vitrinites isolated from the coals in basins in NW China have been checked using solid state ¹³C NMR and flash pyrolysis-GC/MS. Study shows some Jurassic collodetrinites are rich in aliphatic products in pyrolysates, consisting with the high amount of methylene carbon in ¹³C NMR spectra. In contrast, pyrolysates of Jurassic collotelinites are rich in phenols and alkylbenzenes. Also one Pennsylvanian and one Permian vitrinite selected from the Ordos basin, NW China have been checked for comparison. The proportion of aliphatics is low in pyrolysates, and aliphatic carbon peak in ¹³C NMR spectrum of Permian vitrinite is mostly composed of gas-prone carbons compared with collodetrinites in those Jurassic basins. But both pyrolysis and ¹³C NMR data shows the Pennsylvanian vitrinite is not only gas-prone but also oil-prone. Relatively high proportion of long chain aliphatic structure of some Jurassic vitrinite in Junggar, Turpan-Hami basins may be due to the contribution of liptodetrinites, which may be included during the formation of vitrinites. And it seems that suberinite is the most possible precursor of long chain aliphatics in the structure of Jurassic collodetrinite.     

火试金法测定铜精矿中金含量结果的不确定度评定

对火试金法测定铜精矿中金含量的结果进行不确定度评定。分析了铜精矿样品称量、铜精矿样品的不均匀性和配料处理,以及金粒称量等因素对金含量测量结果不确定度的影响,并得出火试金法测量铜精矿中金含量的扩展不确定度。     

我国水文标准化工作的现状与发展

到目前为止,由水利部水文局主持和编制的水文站网、水文仪器及设备、水文监测、水文情报与预报、水环境监测与评价(含标准物质)、水资源监测与评价、水文综合管理、水利通信、水利信息化等一系列技术及管理标准和规范共有300多项,这些水文标准和规范是整个水利标准化体系中的重要组成部分,已经基本形成一套能够满足我国水利事业协调发展和安全需要的水文标准化体系,支撑并规范了水文、水利信息化、水利通信等相关工程的有序、科学和健康发展.同时要努力克服在标准化工作中的不足,加大经费投入,培养一批标准化工作精英,为我国的水文标准化工作做出更大的贡献.     

地面沉降在闸涵工程运行中引起的经济损失的探讨

通过对天津市内主要河流上的4座具有代表性的闸涵进行详细的计算分析,得出闸涵水利工程维护费用中约19%属于地面沉降引起的损失,并将此比重推广到全市的闸涵工程,最后计算得出地面沉降造成的损失约为3.45亿元。     

河南桐柏老湾花岗岩体锆石SHRIMP U-Pb年龄及其地质意义

通过对河南桐柏老湾花岗岩体开展锆石SHRIMP U-Pb测年,获得了一个高精度的U-Pb年龄(132.5±2.4)Ma,比较可靠地限定了老湾花岗岩体的形成时代属于燕山中期,而不是前人所认为的燕山晚期.结合区域构造-岩浆活动时间,认为在燕山中期,桐柏及其邻区存在次比较重要的构造-岩浆事件,老湾花岗岩体是这次构造-岩浆活动事件的产物.     

俄罗斯砂岩型铀矿成矿定量预测技术及应用

本文系统地介绍了俄罗斯砂岩型铀矿成矿定量预测技术的基本原理及建立矿床物理-数学模型、成矿预测等关键技术,并在潮水盆地大红山地区进行了成矿预测应用.认为该成矿预测技术丰富了我国铀矿成矿定量预测方法,可作为我国砂岩型铀矿成矿预测的一种新的技术手段.