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.     

有关边界层问题中一类奇异积分方程新的存在性结果

对边界层理论新结果中出现的一类奇异积分方程w（t）=∫1 t（1-s）（λ＋λs＋s）/w（s）ds＋（1-t）∫t 0s/w（s）ds,t∈（0,1）进行讨论，并得出了上述方程在λ∈（-1/2,0）上正解存在性的新结果。     

BACKGROUND STUDY ON NON-RESIDUAL PHASE OF ELEMENTS IN CHANGJIANG AND HUANGHE ESTUARINE SEDIMENTS

Analysis of Zn, Cu, Pb, Co, Cr, Li, Ni, K, Al, Fe extracted by 1 mol/L HCl or 0.5 mol/LHCl/H_2O_2, showed concentrations of Zn, Cu, Pb, Co, Cr, Fe, Ni were significantly correlated with Li, Al,K, and clay. Two methods are used to indicate the background value of the non-residual phase of elementsin sediments, and are the same as the methods used to indicate the background value of totalconcentrations in sediments. The first method uses correlograms and regression equations,the second usesthe mean element concentrations normalized with grain size. Li, Al, K can be used as reference elements to determine the background value of Zn, Cu, Pb, Co,Cr, Ni, Fe, while the clay concentration's correlation with some extractable concentrations can be used tocalculate the background value of the non-residual phase of elements as a percentage of clay concentrationin the sediments. Based on this study, the concept of using the background value of the non-residualphase of elements to compare the pollution level in differ     

抗差估计的初值选择

对L1 范估计的平差值和LS估计的平差值作为抗差估计的初值进行了比较 ,指出在 ρ函数是严凸函数时 ,二者无明显的区别。因为LS估计简单且易于计算 ,因此建议用LS估计的平差值作为抗差估计的初值。     

广西河池地区矿产资源勘查成果展销会情况介绍

１９９５年７月１１日—１２日，广西地矿厅和河池地区行署首次联合举办矿产资源勘查成果展销会，会址设在河池市，与会代表１３０余人。展销会提交大会展销的矿产资源勘查成果３８项，引资勘查项目３８项。经买卖双方三天的洽谈，涉及到的项目２５项，会议期间签订矿产资源勘查成果合同协议４项，愈千万元，取得较好的社会经济效益。为地勘资金多渠道来源开辟了新路子，对促进地勘单位地质找矿工作及勘查成果有偿使用发挥了重要作用     

广西芒场锡多金属矿田稳定同位素组成对矿床成因探讨

笔者曾对广西芒场锡多金属矿田的稳定同位素进行研究。本文根据硫、铅、氢、氧、碳等稳定同位素组成和锶的初始值提供的信息.探讨了矿床成因。并结合矿田矿床地质特征、控矿条件及有关统计参数,参考前人对矿床认识的基础上,修正提出了该矿田混合热液成矿模式,可供类似矿床研究和找矿的参考。     

用遗传算法解算病态方程

对应用遗传算法解决病态方程问题进行了探讨。利用拟合法而不是通过法方程求解参数，从而避免了法方程系数求逆，使病态方程的解答有了较好的结果。通过模拟计算并和其他方法进行比较，证明该方法是可行的和有效的。     

Magnetotelluric investigations for imaging electrical structure of Garhwal Himalayan corridor,Uttarakhand, India

Magnetotelluric investigations have been carried out in the Garhwal Himalayan corridor to delineate the electrical structure of the crust along a profile extending from Indo-Gangetic Plain to Higher Himalayan region in Uttarakhand, India. The profile passing through major Himalayan thrusts: Himalayan Frontal Thrust (HFF), Main Boundary Thrust (MBT) and Main Central Thrust (MCT), is nearly perpendicular to the regional geological strike. Data processing and impedance analysis indicate that out of 44 stations MT data recorded, only 27 stations data show in general, the validity of 2D assumption. The average geoelectric strike, N70°W, was estimated for the profile using tensor decomposition. 2D smooth geoelectrical model has been presented, which provides the electrical image of the shallow and deeper crustal structure. The major features of the model are (i) a low resistivity (<50Ωm), shallow feature interpreted as sediments of Siwalik and Indo-Gangetic Plain, (ii) highly resistive (> 1000Ωm) zone below the sediments at a depth of 6 km, interpreted as the top surface of the Indian plate, (iii) a low resistivity (< 10Ωm) below the depth of 6 km near MCT zone coincides with the intense micro-seismic activity in the region. The zone is interpreted as the partial melting or fluid phase at mid crustal depth. Sensitivity test indicates that the major features of the geoelectrical model are relevant and desired by the MT data.     

Beyond the numbers: Understanding the value of vegetation to rural livelihoods in Africa

Different types of vegetation found in local environments are of value to rural livelihoods of many African households. However, the dominant way of expressing this value, which is mainly through economic valuation studies, is potentially limiting this knowledge’s usefulness in policy and research, due to the inability to present full picture. Using literature review and insights from field work, the paper argues that realized and notional values of vegetation to rural livelihoods are socially constructed and contested, and - in addition to understanding local livelihood context, which include social difference, and ecological dynamics - a focus on social institutions as terrains of negotiation is crucial. This means that resource value in rural livelihoods can be realized through contested and negotiated access arrangements that are mediated by complex institutions at local and external levels.     

攀西地区铂族元素地球化学异常分布及其筛选

攀西地区不同时代地层的水系沉积物中铂族元素地球化学背景值具有明显的规律性分布.笔者通过对该地区铂族元素含量在各地层中的分布以及地球化学异常的圈定、归并和分类,结合有关铂族矿床地球化学特征的认识,通过对水系沉积物中Pt、Pd综合异常的筛选,在铂族元素综合衬值异常图的基础上共划分出Ⅰ类异常4个、Ⅱ类异常19个、Ⅲ类异常23个以及Ⅳ类异常14个.初步筛选出的19个Ⅱ类异常子区可为进一步开展铂族矿床找矿工作提供依据.