郯庐断裂带苏皖段重力变化的多尺度分解与构造活动分析

基于多期绝对重力与相对重力联测资料,获取不同时空尺度的郯庐断裂带苏皖段及周边地区重力场动态变化,采用小波多尺度分解方法计算不同深度的重力变化,结合区域地质构造,从深度层次的横向变化与时间尺度的纵向变化2个方面进行分析。结果表明,研究区重力变化主要由中、上地壳密度变化或断裂构造活动引起。沿郯庐断裂带苏皖段走向断裂活动分布不均,受周边深大断裂带交切作用的影响表现出明显的分段活动特征。淮阴-响水口断裂、滁河断裂等深大活动断裂带构造活动对局部重力变化具有一定的控制作用,沿断裂带走向分布有明显的重力变化梯度带;郯庐断裂带郯城-泗县段、大别山构造带霍山-六安及江苏溧阳-镇江地区在小波细节动态变化中存在重力变化梯度带异常区,但强度不高,考虑到多条深大断裂交切于此,后期需要重点关注。     

Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition

We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm for space-geodetic applications: firstly, we investigate how convergence can be accelerated by means of both subspace and block-diagonal preconditioning. The efficiency of the latter dominates if the design matrix exhibits block-dominant structure. Secondly, we address Tikhonov-Phillips regularization in general. Thirdly, we demonstrate an effective implementation of the algorithm in a high-performance computing environment. In this context, an important issue is to avoid the twofold computation of the design matrix in each iteration. The computational platform is a 64-processor shared-memory supercomputer. The runtime results prove the successful parallelization of the LSQR solver. The numerical examples are chosen in view of the forthcoming satellite mission GOCE (Gravity field and steady-state Ocean Circulation Explorer). The closed-loop scenario covers 1 month of simulated data with 5 s sampling. We focus exclusively on the analysis of radial components of satellite accelerations and gravity gradients. Our extensions to the basic algorithm enable the method to be competitive with well-established inversion strategies in satellite geodesy, such as conjugate gradient methods or the brute-force approach. In its current development stage, the LSQR method appears ready to deal with real-data applications.     

经验模式分解的尺度阈值滤波在变形分析中的应用

本文将信号处理领域的经验模式分解算法应用于变形信息的提取中,通过引入阈值函数,建立了基于经验模式分解的尺度阈值滤波模型,采用优化模型确定了经验模式分解的次数。分别通过模拟试验和实测数据与小波阈值法和多项式拟合法进行了比对,分析表明:在低噪声情况下,三种方法都有一定的滤波效果;在高噪声情况下,经验模式分解的尺度阈值滤波法具有与小波阈值去噪法相等的精度,而且瞬时强噪声识别能力更好,优于多项式拟合法。     

球谐分析及合成并行计算

本文介绍了球谐合成和分析问题的数值计算原理,研究分析了其内在的并行性,提出了区域划分和聚合通信的并行计算策略。基于傅立叶变换技术,应用MPI(消息传递接口)并行编程模式,实现了球谐分析及合成计算的并行化。实验结果表明,较之串行实现,并行实现大大缩短了计算时间,获得了近似线形加速比,取得很高的并行效率。     

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil.     

Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis

Analysis of carbon emission mechanism based on regional perspectives is an important research method capable of achieving energy savings and emission reductions. Xinjiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. Ensuring stable socio-economic development while achieving energy savings and meeting emission reductions targets, is the key issue currently facing the region. This paper is based on the input-output theory, and conducts a structural decomposition analysis on the factors affecting energy-related carbon emissions in Xinjiang from 1997 to 2007; this analysis employs a hybrid input-output analysis framework of “energy - economy - carbon emissions”. (1) Xinjiang’s carbon emissions from energy consumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy resources, the mining of mineral resources, and the processing industry. (2) The analysis of the direct effects of the influencing factors on carbon emissions showed that the change in per capita GDP, the final demand structure, the population scale, and the production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was the important influencing factor in stopping the growth of carbon emissions. This showed that while the sizes of Xinjiang’s economy and population were growing, the economic structure had not been effectively optimized and the production technology had not been efficiently improved, resulting in a rapid growth of carbon emissions from energy consumption. (3) The analysis of the indirect effects of the influencing factors of carbon emission showed that the inter-provincial export, fixed capital formation, and the consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. (4) The growth of investments in fixed assets of carbon intensive industry sectors, in addition to the growth of inter-provincial exports of energy resource products, makes the transfer effect of inter-provincial “embodied carbon” very significant.     

热分解法测定水盐体系中硝酸铵和水含量研究

针对复杂硝酸铵水盐体系溶解度的测定,传统分析方法操作步骤繁琐,且试剂较贵,引入一种简单准确的分析方法,即热分解法,对LiNO3-KNO3-NH4NO3-H2O体系和NaNO3-KNO3-NH4NO3-H2O体系中硝酸铵和水的含量进行精确分析。结果表明,热分解温度控制在230～240℃,若控制样品质量为1.5 g,分解时间不低于36 h,能将LiNO3-KNO3-NH4NO3-H2O体系中的硝酸铵和水彻底分解,且随着样品中硝酸铵含量增加,热分解时间也将延长,分析相对误差能控制在0.2%以内。针对复杂NaNO3-KNO3-NH4NO3-H2O体系,热分解温度控制在230～255℃,若控制样品质量为1.5 g,分解时间不少于44 h,且随着样品中硝酸铵含量的增加,相应延长热分解时间,能将复杂NaNO3-KNO3-NH4NO3-H2O体系中的硝酸铵和水彻底分解,分析相对误差能控制在0.2%以内。     

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.     

武汉市碳排放的测算及影响因素分解研究

研究基于14个主要方面碳源,测算了武汉市1996—2009年碳排放量。发现自1996年以来武汉市碳排放呈现"平稳—上升—平稳"的三阶段特征。进一步基于LMDI模型分解碳排放的影响因素,结果表明,能源结构、能源效率因素对碳排放量具有一定抑制作用,但效果不够显著且波动性较强,1997—2009年与基期相比,能源结构、能源效率因素分别累计实现了0.95%,11.30%的碳减排;而经济因素与人口规模因素则对碳排放具有较强推动作用,分别累计产生了64.65%,19.65%的碳增量。最后,据此提出推进武汉市碳减排的对策建议。     

结合被动微波和光学遥感的“超级寒潮”广东 地表温度特征及其对马铃薯生长的影响

利用被动微波可穿透云层的优势,基于GCOM-W1/AMSR2（Global Change Observation Mission-Water/ Advanced Microwave Scanning Radiometer 2）星载被动微波传感器的多通道亮度温度数据,反演了2016年1月下旬“超级寒潮”过程的广东省每日地表温度,分析了寒潮过程中广东省地表温度的时空变化特征。结果表明：在寒潮前后,广东省地表温度呈现出明显的先下降而后上升的趋势,寒潮过程带来的地表最低温度在广东北部大部分地区达到了277 K（4℃）,西南大部分地区为280~282 K（7~9℃）,其余大部分地区278~279 K（5~6℃）;降温幅度在广东南部地区达到了8~12 K（8~12℃）,北部大部分地区为5~7 K（5~7℃）。此外,利用归一化植被指数（NDVI）对植被生长的指示作用,基于HJ-1 A/B卫星的CCD传感器的多通道反射率数据,计算了近3年冬季广东省典型马铃薯种植区域马铃薯叶片的NDVI值,结果表明：马铃薯叶片的NDVI值在此次寒潮过程后出现了明显的下降,大部分地区（55.4%）马铃薯NDVI降低了0.1~0.2,部分地区（17.2%）马铃薯NDVI下降了0.2~0.3;进一步对比此次“超级寒潮”后同时段的前2年数据,发现寒潮后马铃薯叶片NDVI相对于正常年份也低了0.2,表明马铃薯叶片NDVI下降的主要原因是寒潮带来的低温,而不只是马铃薯的正常老化现象。NDVI的降低表明大部分地区马铃薯生长受到了抑制,马铃薯生长状况的这一变化也通过现场调查得到了证实。