运用策略路由技术保障新疆测震台网数据的稳定传输

为保证测震台网数据稳定可靠传输,提高本区域行业网络带宽利用率,分析了行业网络3条上行链路带宽占用情况及拥塞原因,运用策略路由技术,在网络边界设备上制定并应用规则改变了数据包的选路。通过对比分析前后获取的流量图,说明在设备上使用策略路由技术可以较好地均衡使用3条上行链路,保障测震数据畅通传输。     

冰雷达探测数据处理方法研究——以南极冰盖Dome A地区的数据处理为例

提出了冰雷达数据处理的流程,详细论述了冰雷达数据处理中常规修正的关键技术：静态校正、增益控制、带通滤波以及偏移处理等。以中国第24次南极科学考察（CHINARE 24）所获取的Dome A 地区30 km×30 km范围冰雷达原始数据处理作为实例,提取得到了该区域冰盖内部等时层埋深以及冰盖的冰厚数据。通过插值展现了冰盖等时层以及冰岩界面的三维空间形态。结合该区域ICESat冰盖表面高程数据,构建出了Dome A地区涵盖冰盖表面、冰盖等时层以及冰岩界面形态特征的三维模型。     

The role of sub‐national government and the private sector in future spatial data infrastructures

A Spatial Data Infrastructure (SDI) facilitates and coordinates the exchange and sharing of spatial data between stakeholders in the spatial data community. With this objective in mind, countries throughout the world are developing SDIs to manage and utilize their spatial data assets more effectively. These countries are developing SDIs to assist in various kinds of decision‐making at different levels of government jurisdictions that have an important impact within their national boundaries. However, current research shows that SDI is understood and described differently by stakeholders from different disciplines and different jurisdictional levels. Therefore, in many cases, SDI initiatives remain very much an innovation, even among practitioners. There are still uncertainties regarding the benefits and identities of SDIs, particularly in connection with how they evolve over time to meet user needs. This paper reviews and assesses the development of SDIs throughout the world over the past 15 years and the leadership role of national governments in SDI creation. This assessment is based on the SDI activities of various jurisdictions including Asia‐Pacific, Australia, North America, and Europe, and research into the worldwide effects of spatial information clearing houses. This assessment includes a discussion on emerging trends in SDI development, with particular reference to the increasingly important role played by sub‐national governments and the private sector within the framework of SDI development. The paper concludes with a discussion of the implications for future SDI development, including the delivery of a virtual world that has a particular focus on facilitating decision‐making at a community level within a national context.     

A spatiotemporal data model for river basin‐scale hydrologic systems

Despite a long history of synergy, current techniques for integrating Geographic Information System (GIS) software with hydrologic simulation models do not fully utilize the potential of GIS for modeling hydrologic systems. Part of the reason for this is a lack of GIS data models appropriate for representing fluid flow in space and time. Here we address this challenge by proposing a spatiotemporal data model designed specifically for large‐scale river basin systems. The data model builds from core concepts in geographic information science and extends these concepts to accommodate mathematical representations of fluid flow at a regional scale. Space–time is abstracted into three basic objects relevant to hydrologic systems: a control volume, a flux and a flux coupler. A control volume is capable of storing mass, energy or momentum through time, a flux represents the movement of these quantities within space–time and a flux coupler insures conservation of the quantities within an overall system. To demonstrate the data model, a simple case study is presented to show how the data model could be applied to digitally represent a river basin system.     

Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems.     

Seasonal climatologies of oxygen and phosphates in the Bering Sea reconstructed by variational data assimilation approach

Climatological fields of dissolved oxygen and phosphates in the Bering Sea during the spring, summer, and fall seasons were generated on the basis of an extensive dataset of hydrochemical observations (16,356 stations, beginning in 1928) and a novel 3D variational algorithm for interpolation of a passive ocean tracer. The resulting patterns comply with maps produced earlier using an optimal interpolation method, though they also provide more detail and contain no “missing data” regions. Vertical, spatial, and temporal variability of both parameters follow large-scale patterns of circulation, upper mixed layer depth, and phytoplankton productivity in the Bering Sea.     

Directional statics in common reflection surface stack for rugged surface topography

Seismic data acquired along rugged topographic surfaces present well‐known problems in seismic imaging. In conventional seismic data processing, datum statics are approximated by the surface consistence assumption, which states that all seismic rays travel vertically in the top layer. Hence, the datum static for each single trace is constant. In case this assumption does not apply, non‐constant statics are required. The common reflection surface (CRS) stack for rugged surface topography provides the capability to deal with this non‐vertical static issue. It handles the surface elevation as a coordinate component and treats the elevation variation in the sense of directional datuming. In this paper I apply the CRS stack method to a synthetic data set that simulates the acquisition along an irregular surface topography. After the CRS stack, by means of the wavefield attributes, a simple algorithm for redatuming the CRS stack section to an arbitrarily chosen planar surface is performed. The redatumed section simulates a stack section whose acquisition surface is the chosen planar surface.     

Different levels of macroalgal sampling resolution for pollution assessment

The effects of using reduced sampling resolutions to study macroalgal vegetation patterns have not been studied sufficiently. Here, we test the influence of taxonomic resolution level, removal of occasional species, aggregation of species abundances into functional groups and data transformation in the detection of a long-term recovery process by phytobenthic intertidal assemblages. Results indicate that the aggregation of species data into the genus level has very little influence. Likewise, almost any significant information is lost when occasional algae are removed. Analyses at the level of families and orders still clearly detect differences between highly degraded and reference vegetation. By contrast, analyses based on class and functional group abundances capture quite different information. The effect of transformation is similar at the different taxonomic levels. Most surrogate measures properly reflect changes in diversity. It is concluded that genus level is the most appropriate surrogate approach for detecting the recovery process.     

基于预测编码算法的地震勘探数据无失真压缩理论与方法

海量数据给地震勘探数据的传输、存储和处理提出了严峻挑战.地震数据压缩是解决海量地震数据传输和存储问题的关键.本文定义了SEG Y地震勘探数据文件中的头段数据所占比例(简称头段比例),给出了头段比例计算式,并导出了SEG Y文件压缩倍数与头段比例、头段压缩倍数和样点压缩倍数之间的关系式,从而发现SEG Y文件压缩倍数随样点压缩倍数变化的理论极限是头段压缩倍数与头段比例之比值,并据此从理论上阐明了对头段数据进行高效无失真压缩的必要性.更重要的是,本文对SEG Y数据文件中的头段数据进行了研究,发现了卷头数据和道头数据各自的统计规律,为对头段数据实现高倍压缩提供了重要的理论依据.在此基础上,本文提出了一种适合于对SEG Y头段数据进行高效压缩的方法,实验结果表明,在保证无失真的情况下,本文方法可对SEG Y头段数据实现30~1000倍的压缩,这远高于用Winzip和WinRAR压缩SEG Y头段数据所达到的压缩倍数.     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.