Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

A comprehensive parametric approach to study the probability distribution of rainfall data at scales of hydrologic interest (e.g. from few minutes up to daily) requires the use of mixed distributions with a discrete part accounting for the occurrence of rain and a continuous one for the rainfall amount. In particular, when a bivariate vector (X, Y) is considered (e.g. simultaneous observations from two rainfall stations or from two instruments such as radar and rain gauge), it is necessary to resort to a bivariate mixed model. A quite flexible mixed distribution can be defined by using a 2-copula and four marginals, obtaining a bivariate copula-based mixed model. Such a distribution is able to correctly describe the intermittent nature of rainfall and the dependence structure of the variables. Furthermore, without loss of generality and with gain of parsimony this model can be simplified by some transformations of the marginals. The main goals of this work are: (1) to empirically explore the behaviour of the parameters of marginal transformations as a function of time scale and inter-gauge distance, by analysing data from a network of rain gauges; (2) to compare the properties of the regression curves associated to the copula-based mixed model with those derived from the model simplified by transformations of the marginals. The results from the investigation of transformations’ parameters are in agreement with the expected theoretical dependence on inter-gauge distance, and show dependence on time scale. The analysis on the regression curves points out that: (1) a copula-based mixed model involves regression curves quite close to some non-parametric models; (2) the performance of the parametric regression decreases in the same cases in which non-parametric regression shows some instability; (3) the copula-based mixed model and its simplified version show similar behaviour in term of regression for mid-low values of rainfall. An erratum to this article can be found at     

三维F－K域DMO方法及应用效果

在常速情况下，从三维时距方程出发，通过倾角代换，将三维倾角时差分裂为两个正交方向上的二维倾角时差，然后在这两个正交方向上分别做一次二维倾角时差校正，便可达到三维倾角时差校正的目的．应用实例表明这种三维ＤＭＯ分裂方法是切实有效的．     

基于U型网络的X线头影图像特征点自动定位

为了提高口腔临床诊断效率,提出一种基于U型生成对抗网络的X线头影测量图像结构特征点自动定位方法。该方法在生成对抗网络框架下构建U型生成器网络,用于学习从当前图像到目标特征点偏移距离图的映射;再构建鉴别器网络以判断预测的偏移距离图是否与真实数据一致。将新采集的X线头影测量图像作为已训练的U型生成器网络输入,得到新图像针对目标特征点的偏移距离图,然后通过回归投票方法从预测出的偏移距离图中获得检测目标特征点坐标。实验结果表明,该方法相较于其他方法具有更高的检测成功率,能较准确地获得X线头影测量图像中结构特征点的位置。     

太阳风压力系数的研究

利用全球磁流体力学(MHD)的模拟结果,研究了太阳风压力系数与上游太阳风参数和日下点磁层顶张角的相关性.在识别出日下点附近磁层顶位置后,通过拟合得到日下点附近的磁层顶张角.在考虑上游太阳风中的磁压和热压以及磁层顶外侧的太阳风动压的情况下,计算了太阳风压力系数.通过分析行星际磁场不同方向时太阳风动压在日地连线上与磁压和热压的转化关系,详细研究了太阳风参数和日下点磁层顶张角对太阳风压力系数的影响,得到以下相关结论:(1) 在北向行星际磁场较大(Bz≥5 nT)时,磁层顶外侧磁压占主导,南向行星际磁场时磁层顶外侧热压占主导;(2) 太阳风压力系数随着行星际磁场的增大而增大,随着行星际磁场时钟角的增大而减小;并且在行星际磁场大小和其他太阳风条件相同时,北向行星际磁场时的太阳风压力系数要大于南向行星际磁场时的;北向行星际磁场时,太阳风压力系数随着太阳风动压的增大而减小,南向行星际磁场时,太阳风压力系数随着太阳风动压的增大而增大;以上结论是对观测结果的扩展;(3) 最后,我们还发现太阳风压力系数随着日下点磁层顶张角的增大而增大.     

沈阳地震台大震速报震级差分析

利用沈阳台测算震级与国家台网速报标准震级进行比较,寻找存在震级差的原因,分析得出,台站测算震级存在误差的原因主要有震中距,方位角,地震震级等。     

陕甘宁晋地区黄土地震滑坡致灾距的初步研究

通过对陕甘宁晋地区黄土滑坡震害资料的分析,提出了地震滑坡最大致灾距的确定方法。依据区内８０个地震的滑坡震害资料的非线性拟合,初步建立了地震震级与地震滑坡最大致灾距的定量关系。     

中国地震速报台网走时残差分析与走时修正

使用了３０个速报台站的观测数据,计算了１００多个中国境内外的地震事件初至波走时残差。在此基础上分析各台所记录的所有地震事件的残差随方位角和震中距的分布,及产生这些残差的地球深部非流体静力平衡状态和地球表面构造等方面的原因,并给出了不同区域地震走时的平均校正值,从而提高了地震定位精度。     

铁路隧道火灾中烟气逆流层长度的研究

以狮子洋隧道为工程背景,对不同火源热释放速率、不同通风风速、不同坡度及不同断面当量直径情况下的火灾进行了数值模拟。分析了隧道烟气逆流层长度的变化规律,并对模拟数据进行了拟合。结果表明,隧道烟气逆流层长度与通风风速、火源热释放速率、隧道断面当量直径的自然对数值拟合均符合直线关系,呈递增或递减变化;坡度对烟气逆流层长度的影响随通风风速的增大而减弱。在分析烟气逆流层长度变化规律的基础上,建立了烟气逆流层长度与火源热释放速率、通风风速及断面当量直径的关系式,通过对数据拟合获得了烟气逆流层长度公式,该公式推导合理,并有所创新。     

Evaluation of earthquake signal characteristics for early warning

This paper evaluates different characteristics for earthquake early warning.The scaling relationships between magnitude,epicenter distance and calculated parameters are derived from earthquake event data from USGS.The standard STA/LTA method is modified by adding two new parameters to eliminate the effects of the spike-type noise and small pulsetype noise ahead of the onset of the P-wave.After the detection of the P-wave,the algorithm extracts 12 kinds of parameters from the first 3 seconds of the P-wave.Then stepwise regression analysis of these parameters is performed to estimate the epicentral distance and magnitude.Six different parameters are selected to estimate the epicentral distance,and the median error for all 419 estimates is 16.5 km.Four parameters are optimally combined to estimate the magnitude,and the mean error for all events is 0.0 magnitude units,with a standard deviation of 0.5.Finally,based on the estimation results,additional work is proposed to improve the accuracy of the results.     

A Preliminary Interpretation of Near-field Observation Data of the Zhangbei Earthquake

In this paper, a preliminary data processing was made for tbe aftershock records or theZhangbei M6.2 earthquake on Jan. 10, 1998. The hypocenters of 205 earthquakes with M_L≥0.5 were located. The result shows that most aftershockS were concentrated in a more than10 km long and 5～8 km wide NE-trending beIt. The source mechanism solutions of largeraftersbocks were also calculated, from which it can be inferred that the faulted zone of themain shock is an NE-trending fault. Furthermore, the spectral analysis of P waves was made.The corner frequency is about 10 Hz.