曲江断裂断面结构及地震地质意义

本文在野外地质调查的基础上，从曲江断裂两盘的地层分布、产状变化、破碎带宽度的异常分布等方面对曲江断裂的断面结构进行了初步分析，认为曲江断裂上存在两个尺度较大的凹凸体。结合该断裂的右旋运动特点，对曲江断裂破碎带异常展宽段及串珠状小盆地的成因给予了解释，同时认为曲江断裂上三次７级以上强震的震中与凹凸体的位置明显相关。     

探测液体污染的地质雷达

通过介绍美国中西部一个实验场地下水位之上的石油物质的情况，证明了地质雷达（ＧＰＲ）作为探测地面附近污染的的一种手段重要作用，一些试验表明，ＧＰＲ可以提供测绘渗流层中烃类物质的方法，俄州立大学一个砂质试验坑中的控制测量的结果确实表明，在柴油容器和装有柴油饱和的砂质材料的容器上方丰在的清晰的ＧＰＲ异常。在印第安纳州北部一个汽油泄漏场地，一年中不同时期（夏，秋，冬）测得的ＧＰＲ资料在对比给出了在高度潮湿     

粗网格模式中仍然存在赤道Kelvin波

最近几年，许多科学家一直在用低分辨率的耦合模式研究ＥＮＳＯ现象，一般认为，在粗网格海洋模式中不存在海洋赤道Ｋｅｌｖｉｎ波，这种波也就不能作为模式中存在的ＥＮＳＯ现象的形成机制，在本文中我们将证明在粗网格模式中也存在赤道Ｋｅｌｖｉｎ波。     

MAROS: a decision support system for optimizing monitoring plans

The Monitoring and Remediation Optimization System (MAROS), a decision-support software, was developed to assist in formulating cost-effective ground water long-term monitoring plans. MAROS optimizes an existing ground water monitoring program using both temporal and spatial data analyses to determine the general monitoring system category and the locations and frequency of sampling for future compliance monitoring at the site. The objective of the MAROS optimization is to minimize monitoring locations in the sampling network and reduce sampling frequency without significant loss of information, ensuring adequate future characterization of the contaminant plume. The interpretive trend analysis approach recommends the general monitoring system category for a site based on plume stability and site-specific hydrogeologic information. Plume stability is characterized using primary lines of evidence (i.e., Mann-Kendall analysis and linear regression analysis) based on concentration trends, and secondary lines of evidence based on modeling results and empirical data. The sampling optimization approach, consisting of a two-dimensional spatial sampling reduction method (Delaunay method) and a temporal sampling analysis method (Modified CES method), provides detailed sampling location and frequency results. The Delaunay method is designed to identify and eliminate redundant sampling locations without causing significant information loss in characterizing the plume. The Modified CES method determines the optimal sampling frequency for a sampling location based on the direction, magnitude, and uncertainty in its concentration trend. MAROS addresses a variety of ground water contaminants (fuels, solvents, and metals), allows import of various data formats, and is designed for continual modification of long-term monitoring plans as the plume or site conditions change over time.     

A Measurement of Omega from the North American Test Flight of Boomerang

We use the angular power spectrum of the cosmic microwave background, measured during the North American test flight of the Boomerang experiment, to constrain the geometry of the universe. Within the class of cold dark matter models, we find that the overall fractional energy density of the universe Omega is constrained to be 0.85相似文献   

腾冲火山活动监测,预测与对策研究中的若干重要问题

《膳冲火山活动监测、预测与对策研究》项目是一项集火山活动监测研究、防灾及资源综合开发与利用为一体的系统工程。需要火山地质、地震、地球化学、地球物理、地壳形变及地热等诸多相关学科的合理配置和联合攻关。在过去的３０年,众多学和研究实体对腾冲火山进行 大量的观察和研究获得一批与火山活动的相关的基础研究成果。其中也不乏说明腾冲火山存在在喷发危险性的证据。为了保证本重大项目能在有限时间内及有限的人力、财务     

Latin hypercube approach to estimate uncertainty in ground water vulnerability

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability.