方差分量估计简化公式新探

给出方差分量估计的一个新的简化公式-多余观测分量平均匹配公式，讨论其统计性质，并通过实例验证其实用性。     

试用累积求和方法研究地震活动平静现象及大震概率预报

以表征区域地震活动强度背景的震级期望值作为单个地震事件的目标值,利用震级累积和C值随时间的变化分析地震活动相对平静现象,并给出其显著性检验. 文中还定量分析了平静异常与大震的关系,提出了利用核函数对大震发生时间进行概率外推的方法. 用上述方法对华北区的山西、张家口-渤海地震带的部分地区及新疆区域进行计算,显示该方法能够描述地震活动平静现象,并可合理地对未来大震发生时间进行概率外推估计.     

双线偏振雷达的降水估测Ⅱ. 误差分类分析

详细地定量分析了降水估测中的ＺＲ关系和各类误差。由此可知,指数函数形式与幂函数形式的ＺＲ关系之间无明显差别;ＺＨ的参数因子ＡＨ不能太大;双线偏振雷达的测量误差导致的降雨强度估测误差在各类误差中最小;ＺＲ关系的不可拟合误差之标准差约为０．２１５,因而不容忽视。降雨强度的估测误差可认为呈一偏差逼近于０而标准差约为０．２６５的近正态分布,这对提高区域降水的估测精度尤为重要。     

线性模型的泛最小二乘法

本文首先针对线性模型提出了泛最小二乘法,在设计矩阵不加限制的情形下,得到了参数的泛最小二乘估计量。该方法既发扬了最小二乘法的优点,又克服了它的一些不足,它包含了常见的岭估计和最小二乘估计法;其次讨论了泛最小二乘法的理论依据;接着研究了泛最小二乘估计量的一些统计性质,并与最小二乘估计进行比较,在一定意义上前者优于后者;然后讨论了平衡参数的选取问题;最后,给出一个应用,说明了泛最小二乘法的有效性和可行性。     

基于经验模型和物理模型研究2013 M_S7.0芦山地震余震序列

基于中国地震台网中心2013 MS7.0芦山地震余震数据我们首先确定了余震空间分布范围并根据G-R关系计算了主震后半小时内的完备震级Mc=3.5,并且得到了ML≥3.5和ML≥3.0的地震在2001年至芦山地震前的背景场地震发生率.通过Omori-Ustu经验定律和两种Dieterich模型对芦山地震余震发生率的拟合,我们发现阶梯型Dieterich模型只能模拟p=1的情况,从而造成了模拟曲线与观测数据的差别;前人研究表明震后滑移同样是产生余震的原因,如果假设余震序列由主震静态剪应力Δτ和震后滑移共同作用所产生,我们数值模拟得到的对数型Dieterich模型能够较好地推断余震发生率R随时间t增加而衰减的趋势,能够从物理机制上解释MS7.0芦山地震余震序列衰减指数大于1这一现象.通过对数型Dieterich模型的拟合并结合Andrews的方法,我们还得到MS7.0芦山地震Aσ约为0.155 MPa,ta约为8.4年,这一值与前人研究结果十分接近.     

Petrology and geochemistry of ultrahigh‐temperature granulites from the South Altay orogenic belt,northwestern China: Implications for metamorphic evolution and protolith composition

Ultrahigh‐temperature (UHT) granulites in the South Altay orogenic belt of Northwestern China provide important clues for the lower crustal components and tectonic evolution of the Central Asian Orogenic Belt during the Paleozoic. In this paper, we studied whole‐rock geochemistry and mineral characteristics to understand the protolith and metamorphic evolution of the Altay UHT granulite. The Altay granulite shows negative discriminant function values (DF) of ?9.27 to ?3.95, indicating a sedimentary origin, probably an argillaceous rock. The peak metamorphic temperature–pressure conditions of 920–1010 °C and > 9 kbar were estimated from the geothermobarometry, together with the stability of spinel (low ZnO) + quartz and orthopyroxene (Al₂O₃ up to 9.2 wt.%) + sillimanite + quartz in the Altay UHT rock, indicate a UHT metamorphic condition has been achieved. Two stages of retrograde conditions are recognized in these rocks; the first is an isothermal decompression to approx. 750 °C at 5.2–5.8 kbar at the early stage, and the second is the cooling down to 520–550 °C at 4.8–5.2 kbar. Combined with previous study, the formation of the Altay UHT pelitic granulite with a clockwise retrograde P–T path is inferred to be related with collisional and accretional orogenic process between the Siberian and Kazakhstan–Junggar plates.     

应用杨赤中滤波推估法计算库容量

应用杨赤中滤波推估法,利用二项系数加权游动平均对观测值序列进行逐遍滤波,建立估值数学模型,求得各个方格中心高程估值.再由水库工程的溢洪道高程设计值与各方格中心高程的差值,求得各方格的体积,汇总即得库容量.讨论了不规则边界处理算法.     

测站气温与气压对GPS定位结果的影响

分别利用全球气压气温模型GPT和GPT2处理相同的连续观测站数据。对比两组测站位置估值（简称位置）发现,测站气压偏差所导致的先验天顶延迟偏差不能完全被延迟参数所吸收,导致测站垂向位置出现偏差,且偏差的大小与测站纬度及数据处理中所采用的数据权重有关。测站气温偏差对测站位置的影响可忽略不计,但GPS定位结果的内符合精度随着测站气温的升高而降低。     

Inversion of potential field data using the structural index as weighting function rate decay

Nonparametric inverse methods provide a general framework for solving potential‐field problems. The use of weighted norms leads to a general regularization problem of Tikhonov form. We present an alternative procedure to estimate the source susceptibility distribution from potential field measurements exploiting inversion methods by means of a flexible depth‐weighting function in the Tikhonov formulation. Our approach improves the formulation proposed by Li and Oldenburg (1996, 1998) , differing significantly in the definition of the depth‐weighting function. In our formalism the depth weighting function is associated not to the field decay of a single block (which can be representative of just a part of the source) but to the field decay of the whole source, thus implying that the data inversion is independent on the cell shape. So, in our procedure, the depth‐weighting function is not given with a fixed exponent but with the structural index N of the source as the exponent. Differently than previous methods, our choice gives a substantial objectivity to the form of the depth‐weighting function and to the consequent solutions. The allowed values for the exponent of the depth‐weighting function depend on the range of N for sources: 0 ≤N≤ 3 (magnetic case). The analysis regarding the cases of simple sources such as dipoles, dipole lines, dykes or contacts, validate our hypothesis. The study of a complex synthetic case also proves that the depth‐weighting decay cannot be necessarily assumed as equal to 3. Moreover it should not be kept constant for multi‐source models but should instead depend on the structural indices of the different sources. In this way we are able to successfully invert the magnetic data of the Vulture area, Southern Italy. An original aspect of the proposed inversion scheme is that it brings an explicit link between two widely used types of interpretation methods, namely those assuming homogeneous fields, such as Euler deconvolution or depth from extreme points transformation and the inversion under the Tikhonov‐form including a depth‐weighting function. The availability of further constraints, from drillings or known geology, will definitely improve the quality of the solution.     

Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government’s monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.