藏北羌塘盆地晚三叠世地层特征与对比

羌塘盆地上三叠统日干配错组、土门各拉组和肖茶卡组、那底岗日组分别分布于南、北羌塘凹陷。在以往研究和笔者在中国地质调查局青藏重点沉积盆地油气资源潜力分析项目工作基础上,通过对各地层单元的详细研究和区域对比,从古生物、岩石组合、沉积系列、地层单元的接触关系、年代地层进行了综合分析。结果显示北羌塘凹陷那底岗日组与南羌塘凹陷日干配错组和土门各拉组的时代主要为晚三叠世诺利期—瑞替期,三者为羌塘晚三叠世诺利期—瑞替期裂陷盆地形成过程中的同期异相的火山-沉积系列。北羌塘凹陷肖茶卡组的是残留陆表海沉积产物,其时代主要为晚三叠世卡尼期-诺利期早期,与那底岗日组、日干配错组和土门各拉组均为上下对比关系。     

High-performance three-horizon composition algorithm for large-scale terrains

This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.     

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data–task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

全国前兆台网“九五”系统台站接入的设计与实施

介绍了“全国前兆台网‘九五’系统台站接入改造”项目的技术方案,即采用接入地震前兆通信协议转换器或更换“九五”前兆公用数据采集器方式将“九五”前兆仪器接入“十五”前兆系统,并详述了该方案的技术特点;介绍了方案的部署实施情况,测试平台运行情况以及通过对测试平台的监控而发现并解决的问题.并行运行结果显示,接入改造系统运行稳定,产出数据可靠.     

施甸盆地三维地震动研究

本文利用三维施甸盆地模型以及川滇地区的地壳速度结构,采用谱元法和并行计算技术,考虑地形的影响,模拟了盆地外围中等设定地震作用下施甸盆地地震波的传播,分析了面波的产生和传播,研究了该盆地不同位置剖面地震动的差异和原因.研究表明,小型盆地在中等地震的作用下盆地内部地震动空间差异很大,盆地中较深的小型凹陷更容易汇聚面波,形成更强的振动,位于覆盖层厚度剧烈变化区域的地震动强度也会很大,甚至大于覆盖层厚的区域.较基岩地震动,盆地内地震动持续时间明显增加,地震动的放大作用明显,这也是盆地震害严重的主要原因之一.     

大区域地下水模拟的预优并行GMRES(m)算法研究

大区域研究区由于涉及范围大、水文地质参数复杂多变,一直是进行地下水数值模拟的热点和难点。针对大区域地下水模拟的特点,在MPI环境中对Krylov子空间GMRES(m)算法的并行性进行分析,提出基于区域分解法的并行实现策略,并对不同的预条件子的加速效果进行比较。数值实验结果表明:并行GMRES(m)算法在求解大区域三维地下水模型时可以显著的加快求解速度,且具有较好的可扩展性。另外,Jacobi预条件子与GMRES算法的组合具有更优的加速比和执行效率,是一种求解大型化、复杂化地下水水流问题的可行方案。     

基于集群的影像并行OPTA算法研究

并行计算是提高海量遥感影像细化处理速度的有效途径.基于对OPTA细化算法的分析,深入研究OPTA细化并行算法中数据划分和数据通信优化等关键问题,提出一种适合集群并行处理的通信优化并行算法,实验证明该算法具有良好的加速比,适合集群计算环境下海量遥感影像的细化处理.     

GPS软件接收机精确载波频率捕获算法分析与实现

采用基于快速傅里叶变换（FFT）的并行码相位搜索捕获算法实现GPS信号的捕获,根据相位关系对载波频率进行精化,使其满足跟踪模块的需求。在分析算法的基础上,通过对模拟信号和实测数据进行捕获实验,实现了对载波频率的精化,获得精确频率可直接用于后续跟踪环路。     

利用流水线技术的遥感影像并行处理

针对现有的OpenMP和直接的线程并行法难以对遥感影像处理链进行加速的问题,引入流水线概念对复杂的功能链进行并行加速,提出了基于流水线技术的遥感影像并行处理方法。影像处理实验验证了流水线在遥感影像多级连续处理方面的优势。