Clustering and visualization of earthquake data in a grid environment

We present a web client-server service WEB-IS, which we have developed for remote analysis and visualization of seismic data consisting of both small magnitude events and large earthquakes. We show that the problem-solving environment (PSE) intended for prediction of large magnitude earthquakes can be based on this WEB-IS idea. The clustering schemes, feature generation, feature extraction techniques and rendering algorithms form a computational framework of this environment. On the other hand, easy and fast access both to the seismic data distributed among distant computing resources and to computational and visualization resources can be realized in a GRID framework. We discuss the usefulness of NaradaBrokering (iNtegrated Asynchronous Real-time Adaptive Distributed Architecture) as a middleware, allowing for flexibility and high throughput for remote visualization of geophysical data. The WEB-IS functionality was tested both on synthetic and the actual earthquake catalogs. We consider the application of similar methodology for tsunami alerts.     

Improved algorithms for generalized finite element simulations of three-dimensional hydraulic fracture propagation

This paper reports improvements to algorithms for the simulation of 3-D hydraulic fracturing with the Generalized Finite Element Method (GFEM). Three optimizations are presented and analyzed. First, an improved initial guess based on solving a 3-D elastic problem with the pressure from the previous step is shown to decrease the number of Newton iterations and increase robustness. Second, an improved methodology to find the time step that leads to fracture propagation is proposed and shown to decrease significantly the number of iterations. Third, reduced computational cost is observed by properly recycling the linear part of the coupled stiffness matrix. Two representative examples are used to analyze these improvements. Additionally, a methodology to include the leak-off term is presented and verified against asymptotic analytical solutions. Conservation of mass is shown to be well satisfied in all examples.     

Scaling procedures for heterogeneous unconfined aquifers

Complex flows in heterogeneous confined and unconfined aquifers is a phenomenon that continues to present difficulties in flow mapping and modelling in the field, laboratory, and through numerical simulations. It is often the case with complicated phenomena that transformative scaling and reduction of the problem through symmetry is of great efficacy in the formation of predictive models in both the laboratory and computational settings. A detailed a study of the application of a broad class of Lie scaling transformations on a set of equations representing the groundwater flows in heterogeneous confined and unconfined aquifers has produced a set of scaling relationships between the spatial variables, hydrologic variables, and parameters. The set of scaling transformations preserve the structure of the equations in the sense that the scaling transformations leave the initial‐boundary value system representing the invariant groundwater flows. This theoretical approach elucidates not only the scaling relationships but also the properties that hydrologic variables and parameters must satisfy in order for calling to be possible. Validation of the theory developed is carried out through a series of four numerical simulations using the USGS modflow ‐2005 software package. The results of these experiments demonstrate that the derived scaling transformations can effectively form predictive models of large‐scale phenomena at small scales with negligible error in many cases. Comments on the limitations of the approach and directions for future research are made in the closing sections. Copyright © 2016 John Wiley & Sons, Ltd.     

智慧城市时空信息云平台建设研究

以完善的云环境建设为支撑,以构建标准安全的防护体系为保障,以时空信息云平台建设为基础,以智慧应用建设为重点,促成跨域、跨部门、跨层级的数据融合,提高城市综合智能化水平,使城市运转更加高效、敏捷、低碳与和谐。     

PMODTRAN: a parallel implementation based on MODTRAN for massive remote sensing data processing

MODerate resolution atmospheric TRANsmission (MODTRAN) is a commercial remote sensing (RS) software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth's atmosphere and the radiation observed by a remote sensor. However, when very large RS datasets must be processed in simulation applications at a global scale, it is extremely time-consuming to operate MODTRAN on a modern workstation. Under this circumstance, the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task. This paper presents PMODTRAN, an implementation of a parallel task-scheduling algorithm based on MODTRAN. PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster. In addition, PMODTRAN can distribute tasks with different levels of granularity and has some extra features, such as dynamic load balancing and parameter checking.     

A variable precision rough set approach to knowledge discovery in land cover classification

This paper presents a granular computing approach to spatial classification and prediction of land cover classes using rough set variable precision methods. In particular, it presents an approach to characterizing large spatially clustered data sets to discover knowledge in multi-source supervised classification. The evidential structure of spatial classification is founded on the notions of equivalence relations of rough set theory. It allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary regions. The paper also identifies how approximate reasoning can be introduced by using variable precision rough sets in the context of land cover characterization. The rough set theory is applied to demonstrate an empirical application and the predictive performance is compared with popular baseline machine learning algorithms. A comparison shows that the predictive performance of the rough set rule induction is slightly higher than the decision tree and significantly outperforms the baseline models such as neural network, naïve Bayesian and support vector machine methods.     

高性能计算技术在气象领域的应用

高性能计算通过应用超级计算机与并行处理技术解决复杂的计算问题,是信息技术发展比较迅猛的领域之一。气象应用始终是高性能计算的重要领域,高性能计算技术有效地解决了高分辨率、高精度气象数值预报模式发展限制,在气象预报预测业务中发挥着核心支撑作用。数十年以来,由于数值天气模式研究和业务运行对计算资源的强烈需求,国内外气象领域高性能计算机及应用迅速发展起来。气象领域对高性能计算能力及系统的可靠性需求日益提升。高性能计算技术将与气象预报应用日益融合,相互影响促进,不断创新发展。为满足气象预报预测业务和科研工作需求,中国气象部门将进一步提升高性能计算能力,并致力于优化集约高性能计算系统布局,高效管理计算资源,发挥最大效益。     

Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc Mountains of Tanzania

Estimating tropical biomass is critical for establishment of conservation inventories and landscape monitoring. However, monitoring biomass in a complex and dynamic environment using traditional methods is challenging. Recently, biomass estimates based on remotely sensed data and ecological variables have shown great potential. The present study explored the utility of remotely sensed data and topo-edaphic factors to improve biomass estimation in the Eastern Arc Mountains of Tanzania. Twenty-nine vegetation indices were calculated from RapidEye data, while topo-edaphic factors were taken from field measurements. Results showed that using topo-edaphic variables or vegetation indices, biomass could be predicted with an R² of 0.4. A combination of topo-edaphic variables and vegetation indices improved the prediction accuracy to an R² of 0.6. Results further showed a decrease in biomass estimates from 1162 ton ha^?1 in 1980 to 285.38 ton ha^?1 in 2012. This study demonstrates the value of combining remotely sensed data with topo-edaphic variables in biomass estimation.