平行走滑断层相互作用的粘弹模型和减震作用

研究了二维粘弹性介质中平行走滑断层的相互力学作用及其地震活动性的影响。当一条断层发生滑动,将导致平行断层面上剪切应力减小,因此,可能推迟平行断层发生滑动,推迟时间在几年至几百年的范围内,这取决于发生滑动的断层与平行断层之间的距离,以及平行断层自身应力积累孕育地震过程经历的时间。     

A source of bias in rates of surface soil movement as estimated from marked particles

When estimated from measurements of introduced tracer particles, the rate of surface soil movement tends to be greater than the natural rate for equivalent particles on the same site. This consistent overestimation is greatest in the period following tracer introduction and leads to a measurement bias that may be as high as 300 per cent. The magnitude of the bias decreases with time, as the tracer is incorporated into the surface material, but remains detectable statistically for more than a year on some low-angle sites.     

Improved seismic risk estimation for Bucharest,based on multiple hazard scenarios and analytical methods

Bucharest, capital of Romania, is one of the most exposed big cities in Europe to seismic damage, due to the intermediate-depth earthquakes in the Vrancea region, to the vulnerable building stock and local soil conditions.This paper tries to answer very important questions related to the seismic risk at city scale that were not yet adequately answered. First, we analyze and highlight the bottlenecks of previous risk-related studies. Based on new researches in the hazard of Bucharest (recent microzonation map and ground-motion prediction equations, reprocessed real recorded data) and in vulnerability assessment (analytical methods, earthquake loss estimation software like SELENA and ELER, the recently implemented Near Real-Time System for Estimating the Seismic Damage in Romania) we provide an improved estimation of the number of buildings and population that could be affected, for different earthquake scenarios. A new method for enhancing the spatial resolution of the building stock data is used successfully.     

基于MATLAB的基本矩阵随机采样鲁棒估计

基本矩阵的估算可以分为线性算法、非线性优化迭代算法、鲁棒估计算法三大类,而以鲁棒估计效率、效果最为突出,但是实现比较复杂。本文通过数码相机获取的两张未标定照片,采样互相关系数法和RANSAC算法,在MATLAB中实现了基本矩阵的自动拟合,实验表明拟合精度较高、速度快,为下一步三维场景的自动重建和量测奠定了基础。     

植被净初级生产力模型估算及其对气候变化的响应研究进展

近年来随着遥感和地理信息系统技术的广泛应用,植被净初级生产力研究经历了从小范围的传统测量阶段到大范围的模型估算阶段的重要转变,并参与全球变化研究。其研究手段和研究内容大大拓宽,在植被净初级生产力模型估算以及对气候变化（如温度、降水、ＣＯ₂浓度等）的响应等方面的研究取得了可喜的进展。     

Robust估计的算法优化

本文分析了Ｒｏｂｕｓｔ估计收敛慢的原因，提出以最小二乘平差的残差（Ｖ）为初始信息，用Ｌ１估计的权函数所计算的权力初始权，根据迭代计算的残差的大小，及残差的变化所提供的信息，构造出加速收敛的算法，计算结果表明，较之一般的方法，迭代次数显著减少，辨识杠杆点含粗差的能力有较大提高，即搜索粗差的效率有很大提高。     

Using simulated data to investigate the spatial patterns of obesity prevalence at the census tract level in metropolitan Detroit

Obesity is a serious public health problem in the United States. It is important to estimate obesity prevalence at the local level to target programmatic and policy interventions. It is challenging, however, to obtain local estimates of obesity prevalence because national health surveys such as the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) are not designed to produce direct estimates at the local levels (e.g. census tracts) due to small population samples and the need to preserve individual confidentiality. In this study we address the problem of estimating local obesity prevalence rates by implementing a spatial microsimulation modeling technique to proportionally replicate the demographic characteristics of BRFSS respondents to census tract populations in metropolitan Detroit. Obesity prevalence rates are examined for high and low spatial clusters and studied in relation to the U.S. Department of Agriculture's (USDA) measures of low-income neighborhoods and local food deserts and CDC's measure of healthy and less healthy food environments currently used to target obesity reduction initiatives. This study found that obesity prevalence was largely clustered in the City of Detroit extending north into contiguous suburbs. The spatial patterns of highest obesity prevalence tracts were most similarly aligned with USDA-defined low-income tracts and CDC's less healthy food tracts. The locations of USDA's food desert tracts rarely overlapped with the highest obesity prevalence tracts. This study demonstrated a new methodology by which to assess local areas in need of future obesity interventions.     

综合数据处理方法在评定测距仪实际观测精度中的应用

测距仪检定时乘常数是用频率测量方法取得,加常数及测距标准差是用短基线测定,由于仪器检测例的条件与工程现场条件不一致,检测的标称精度不能真实反映实际观测精度,以标称精度定权处理观测数据,影响测量成果的质量;指出综合运用测量平差与回归分析,可对工程中使用的测距仪实际观测精度进行有效评估为仪器性能判别及测量数据处理准确定权提...     

中国近百年温度曲线的对比分析

 温度变化是全球变化研究中非常重要的基础性问题,在中国近百年温度变化方面,十几年来已取得了明显进展,这首先表现在建立了若干条全国平均气温序列,而几条主要序列间的相关系数在0.73～0.97之间。同时,这些进展也表现在基础资料质量提高、空间覆盖面扩大、序列均一性改善以及结果可靠性提高等方面。对多序列进行综合分析得到的新结果显示,1906-2005年中国的年平均气温上升了（0.78±0.27）℃,2007年是我国近百年来最暖的一年;代表性分析显示,现有的几条中国温度序列中除覆盖完整的序列外,其他序列在1920或1930年代以前可能主要反映中国东部变化情况,但在此之后则能较好地代表全国大部分地区的气候变化特征。     

T -distributed Random Fields: A Parametric Model for Heavy-tailedWell-log Data1

Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they are layer specific and include uncertainty caused by using variance estimates.