首都圈地区中小地震全波形震源机制反演

目前对首都圈地区中小地震的震源机制研究所用的方法主要有P波初动极性法、 振幅比法及波形反演方法。 前两种方法只利用了波形中很少量的信息, 且对于稀疏台阵效果较差, 而传统的波形反演法也仅利用了波形的部分信息, 且对于震级、 数据资料等有一定的要求。 为克服上述传统方法中的局限并进一步提高震源机制解的准确性, 本文采用一种新的综合利用上述各种信息的全波形匹配方法来反演首都圈地区中小地震的震源机制解, 提高反演解的可靠性。 为了检验所使用的全波形匹配震源机制反演方法的可靠性, 我们首先进行了合成数据测试, 结果表明全波形匹配方法可以得到稳定可靠的震源机制解。 利用新的全波形匹配方法计算了2019年4月北京发生的两次地震震源机制解, 并结合活动断裂进行分析。 将新的全波形震源机制反演方法应用于首都圈地区2015年以来的22个地震(2.2≤M≤4.3), 并对22个地震的震源机制解及应力轴分布进行了分析, 与前人研究结果具有较好的一致性。     

Latent lifestyle preferences and household location decisions

Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.

微地震监测技术及应用

微地震监测技术涉及近震分析的定位和地壳结构成像, 微地震监测各种定位方法要构造不同目标函数, 地震定位问题的实质是求目标函数的极小值。 全局定位邻近算法(NA)具有不依赖于模型初始值选择, 不会收敛于局部极小值, 比传统线性近似方法有更高的精度和可靠性。 通过地震数据的震相分析, 走时拾取反演可以获得地震影响区的地震波速度结构, 目前已广泛应用于油、 气田勘探开发, 以及页岩气开发中; 矿山开采中矿震、 岩爆, 煤与瓦斯突出, 承压水突水预测; 水利工程建设坝址、 边坡稳定性和天然滑坡监测等多个领域。     

三种高分辨率格点降水预报检验方法的对比

客观有效的评估高分辨率模式格点降水的预报能力,不仅是模式发展中的基础问题,而且直接关系到目前中国气象局主推的格点天气预报业务。以ECMWF(European Centre for Medium-Range Weather Forecasts)模式高分辨率降水格点预报资料、CMORPH(NOAA Climate Prediction Center Morphing Method)卫星与全国3×104个自动观测站的逐时降水量融合资料为基础,选择2015年6~8月55个降水个例,研究传统检验方法、面向对象MODE(Method for Object-based Diagnostic Evaluation)方法、以及邻域法在高分辨率格点降水预报检验中的适用性及优缺点,以期为高分辨率格点降水的预报性能评估提供参考。主要结论如下:(1)尽管点对点的传统方法在高分辨率格点降水检验中存在一定的局限,但传统方法能够在空间上表现高分辨率格点降水预报技巧的地域性差异,在时间上刻画预报的整体性能,对高分辨率格点预报性能评估仍然具有重要的适用价值;(2)邻域法的显著优点在于一方面能够通过变换邻域窗获得不同空间尺度上的传统预报技巧,另一方面独有的FSS(Fractions Skill Score)技巧评分能够表现预报相对于观测降水在格点数量上的比值,结合FSS和不同邻域窗上的传统技巧评分,可以判别在多大空间尺度上能够获得较好的预报技巧;(3)MODE方法在变换卷积半径的基础上提取降水对象,基于降水对象不仅能统计模式的传统技巧评分和预报性能的尺度变化,还可以表现降水对象的质心距离、轴角、面积、强度、综合收益、位移距离等多种属性,这些属性首先为用户提供了模式预报性能的多视角表现,其次从侧面定量描述了模式对天气系统发展快慢、槽脊强弱等预报误差,具有独特的优势,但如何应用对象属性来提高实际的预报能力还存在一些困难。     

基于邻域算法的瑞利面波垂直-水平振幅比及频散曲线联合反演及应用

瑞利面波垂直-水平振幅比(或ZH振幅比)是一个随频率变化的函数,对于台站下方浅层地壳结构非常敏感,且具有和频散资料不同的深度敏感核,是传统频散反演方法的一个很好的补充,从而可以将基阶瑞利面波的ZH振幅比和面波频散数据联合起来更好地反演获得观测台站下方的速度结构.本文提出了基于邻域算法的面波频散曲线与ZH振幅比联合反演方法,我们进行了基于理论模型的模拟测试,证明了联合反演是一种更为可靠的反演方法,且能更好地约束浅层地壳结构.相比于频散曲线单独反演,联合反演不仅可以精确反演获得地壳的Vs结构,对分层地壳的Vp/Vs也能很好地约束.然后我们将联合反演算法应用于实际测量数据,获得了中国西南昆明台(KMI)下方更为准确的地壳横波速度结构及Vp/Vs模型.     

A method for inversion of layered shear wavespeed azimuthal anisotropy from Rayleigh wave dispersion using the Neighborhood Algorithm

Seismic anisotropy provides important constraints on deformation patterns of Earth's material. Rayleigh wave dispersion data with azimuthal anisotropy can be used to invert for depth-dependent shear wavespeed azimuthal anisotropy, therefore reflecting depth-varying deformation patterns in the crust and upper mantle. In this study, we propose a two-step method that uses the Neighborhood Algorithm(NA) for the point-wise inversion of depth-dependent shear wavespeeds and azimuthal anisotropy from Rayleigh wave azimuthally anisotropic dispersion data. The first step employs the NA to estimate depthdependent VSV(or the elastic parameter L) as well as their uncertainties from the isotropic part Rayleigh wave dispersion data. In the second step, we first adopt a difference scheme to compute approximate Rayleigh-wave phase velocity sensitivity kernels to azimuthally anisotropic parameters with respect to the velocity model obtained in the first step. Then we perform the NA to estimate the azimuthally anisotropic parameters Gc/L and Gs/L at depths separately from the corresponding cosine and sine terms of the azimuthally anisotropic dispersion data. Finally, we compute the depth-dependent magnitude and fast polarization azimuth of shear wavespeed azimuthal anisotropy. The use of the global search NA and Bayesian analysis allows for more reliable estimates of depth-dependent shear wavespeeds and azimuthal anisotropy as well as their uncertainties.We illustrate the inversion method using the azimuthally anisotropic dispersion data in SE Tibet, where we find apparent changes of fast axes of shear wavespeed azimuthal anisotropy between the crust and uppermost mantle.     

Leveraging geotagged Twitter data to examine neighborhood happiness,diet, and physical activity

ObjectivesUsing publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York.MethodsWe utilize 2.8 million tweets collected between February–August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics.ResultsFor a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths.ConclusionsSocial media can be leveraged to provide greater understanding of the well-being and health behaviors of communities—information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.     

Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge

Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. First we present the geometrical features that form the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size to analyze the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Therefore, the method is successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvement for two ICP steps has been noted, and we conclude that our features may not be relevant for very dissimilar object samplings.     

地形起伏度最佳分析区域预测模型

地形起伏度指分析区域内最高点和最低点之差,反映宏观区域内地形的起伏特征,是描述地貌形态的定量指标。确定最佳分析区域是地形起伏度提取算法的核心步骤,以及决定地形起伏度提取结果有效性的关键。本文以全国范围内随机选取的78个实验区域、三种不同尺度的DEM数据作为实验对象,分别进行系列分析区域尺度的地形起伏度计算,建立了基于微观地形特征因子的地形起伏度最佳分析区域预测模型。实验表明：相同区域、不同尺度的DEM数据提取的地形起伏度存在差异,DEM尺度相差较小时,地形起伏度的差异也较小;地形起伏度和实验区域的最大高程、区域高差、平均坡度和平均坡度变率等地形特征因子存在强相关关系;当置信水平为0.05时,预测模型拟合参数的准确率达到95%以上,证明预测模型可以有效地确定最佳分析区域的取值范围。     

基于马尔科夫随机场的岩性识别方法

通过地震反演数据识别岩性,是地震反演的一项基本任务.由于不同岩性的弹性参数范围常常存在一定程度的重叠,所以给岩性识别带来了很大的困难.本文以叠前反演的弹性参数为基础,通过马尔科夫随机场(Markov Random Field简写为MRF)建立先验模型,按照解释好的测井资料,对不同岩性的弹性参数进行统计,得到计算所需的参数,在贝叶斯(Bayesian)框架下建立岩性分类的目标函数,达到岩性识别的目的.通过马尔科夫随机场建立先验模型,能够建立相邻点间的相互作用关系,得到横向上延续的岩性剖面.本文使用一个楔形模型和Marmousi Ⅱ模型对该方法进行了测试,结果表明,该方法有效可行.同时,本文通过加入误差的方法,检验了反演存在误差对识别结果的影响.