基于约束的城市街道网自动综合方法

近年来,基于约束的地图自动综合成为研究的热点。本研究在基于约束的地图自动综合理论的基础上,提出了一种城市街道网自动综合方法。该方法依赖于街道网综合中的约束和改进的动态决策树结构,它很好地将约束融入了特定的数据结构,实现了街道网的渐进式综合。实验证明了方法具备可行性。     

CDN技术在国土资源信息服务系统中的应用——以资源网为例

国土资源信息服务类网站信息服务覆盖广、浏览量大、易受到黑客和病毒的攻击。本文以资源网为例,主要介绍CDN技术在国土资源信息服务系统中的应用。CDN技术的引入,可以大规模多区域加速,保证用户的就近访问,隐藏源站服务器,对于提高国土资源信息服务类网站的响应速度,提高网站的安全性具有重大的现实意义。     

高程控制测量中的混合构网法

针对目前常规水准测量在山区公路高程控制测量中的局限性,本文提出了一种三角高程与常规水准测量相结合的混合构网方法,并通过实例得出,这种方法充分利用了两者的优势,在保证成果的可靠性和精度的同时,大大提高了工作效率,缩短了生产周期。     

矿区控制网布网方案的选择

用计算机模拟法,讨论了控制网不同观测量的平差结果,提出了选择矿区控制网方案的原则.     

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

Channelized and hillslope sediment transport and the geomorphology of mountain belts

This paper uses the results of landscape evolution models and morphometric data from the Andes of northern Peru and the eastern Swiss Alps to illustrate how the ratio between sediment transport on hillslopes and in channels influences landscape and channel network morphologies and dynamics. The headwaters of fluvial- and debris-flow-dominated systems (channelized processes) are characterized by rough, high-relief, highly incised surfaces which contain a dense and hence a closely spaced channel network. Also, these systems tend to respond rapidly to modifications in external forcing (e.g., rock uplift and/or precipitation). This is the case because the high channel density results in a high bulk diffusivity. In contrast, headwaters where landsliding is an important sediment source are characterized by a low channel density and by rather straight and unstable channels. In addition, the topographies are generally smooth. The low channel density then results in a relatively low bulk diffusivity. As a consequence, response times are greater in headwaters of landslide-dominated systems than in highly dissected drainages. The Peruvian and Swiss case studies show how regional differences in climate and the litho-tectonic architecture potentially exert contrasting controls on the relative importance of channelized versus hillslope processes and thus on the overall geomorphometry. Specifically, the Peruvian example illustrates to what extent the storminess of climate has influenced production and transport of sediment on hillslopes and in channels, and how these differences are seen in the morphometry of the landscape. The Swiss example shows how the bedding orientation of the bedrock drives channelized and hillslope processes to contrasting extents, and how these differences are mirrored in the landscape. An erratum to this article can be found at     

GALILEO系统局域完备性监测设计与实现

根据GALILEO系统局域完备性概念和网络RTK的基本理论,建立导航系统局域网络完备性监测理论和算法。算法主要包括网络系统完备性算法和用户完备性算法,内容包括网络误差模型的建立、插值算法的选择、误差改正数完备性指标的计算、完备性指标的验证和用户保护水平的计算等。仿真结果表明,采用网络RTK技术和完备性监测算法,飞机的平面保护水平和垂直保护水平均小于完备性告警门限值,可以满足飞机Ⅱ类和Ⅲ类精密进近的完备性要求,为卫星导航系统在民航中的应用提供完备性理论基础。     

基于免疫粒子群优化算法的影像纹理分类

粒子群优化算法是基于群智能的随机全局优化方法,它源于对鸟群简化社会系统的模拟。为了提高标准粒子群优化算法的收敛性能,将生物免疫系统的记忆能力和多样性引入标准粒子群优化算法,提出一种免疫粒子群优化算法。在提取纹理样本Laws纹理能量模板特征、小波特征等纹理特征的基础上,提出针对分类问题的粒子表达方法和群体寻优策略,实现了基于免疫粒子群算法的纹理分类。实验结果表明,与标准粒子群优化算法相比,免疫粒子群优化算法在获取训练样本类别中心时具有较好的收敛性能,并且基于该算法的影像纹理分类具有较高的分类精度。     

三维地震属性参数在煤层厚度预测中的应用

依托“西部煤炭资源高精度三维地震勘探技术”项目工程,对晋城某矿南翼大巷东南区5m×5m×1ms的三维地震数据体,采用三维地震属性参数预测煤层厚度及其变化规律：沿3煤层、15煤层10ms时窗提取地震属性42种,根据钻孔资料,计算出煤厚与地震属性相关系数;从中优选出相关系数大于0．35的地震属性,其中3煤层9个、15煤层10个;然后进行地震属性互相关分析,优选出与3煤、15煤层厚度相关系数较大的4种属性,建立预测煤厚的BP神经网络模型,分别选取3煤层12个、15煤层4个实测数据作为学习训练和测试样本,以钻孔地震属性作为学习样本,对网络进行训练,最终获得全区煤层厚度。经与预留钻孔成果资料对比,预测精度较高,结果可用。