中国西部地区利用烈度数据估计地震动参数的方法

通过最近收集到的中国西部地区的6次地震的地震动参数和烈度数据,建立了该地区适用于贝叶斯方法的各烈度档P(I|GM)的经验分布。利用2011年发生的四川炉霍地震和新疆伽师地震资料验证了依据烈度数据用贝叶斯方法估算峰值加速度的可行性。研究结果还表明,用贝叶斯方法估计的参数精度与选择的先验概率(衰减关系)显著相关。针对这2次地震,用各烈度档地震动参数的均值法、地震动衰减关系法及贝叶斯法3种方法估算的峰值加速度值与峰值加速度观测值之间的均方根比较表明,用贝叶斯方法估算的参数精度优于用另2种方法所获结果。     

Copula models for frequency analysis what can be learned from a Bayesian perspective?

Large spring floods in the Québec region exhibit correlated peakflow, duration and volume. Consequently, traditional univariate hydrological frequency analyses must be complemented by multivariate probabilistic assessment to provide a meaningful design flood level as requested in hydrological engineering (based on return period evaluation of a single quantity of interest). In this paper we study 47 years of a peak/volume dataset for the Romaine River with a parametric copula model. The margins are modeled with a normal or gamma distribution and the dependence is depicted through a parametric family of copulas (Arch 12 or Arch 14). Parameter joint inference and model selection are performed under the Bayesian paradigm. This approach enlightens specific features of interest for hydrological engineering: (i) cross correlation between margin parameters are stronger than expected , (ii) marginal distributions cannot be forgotten in the model selection process and (iii) special attention must be addressed to model validation as far as extreme values are of concern.     

基于地质统计先验信息的储层物性参数同步反演

本文提出的储层物性参数同步反演是一种高分辨率的非线性反演方法,该方法综合利用岩石物理和地质统计先验信息,在贝叶斯理论框架下,首先通过变差结构分析得到合理的变差函数,进而利用快速傅里叶滑动平均模拟算法（Fast Fourier TransformMoving Average,FFT-MA）和逐渐变形算法（Gradual Deformation Method,GDM）得到基于地质统计学的储层物性参数先验信息,然后根据统计岩石物理模型建立弹性参数与储层物性参数之间的关系,构建似然函数,最终利用Metropolis算法实现后验概率密度的抽样,得到物性参数反演结果。并将此方法处理了中国陆上探区的一块实际资料,本方法的反演结果具有较高的分辨率,与测井数据吻合度较高;由于可以直接反演储层物性参数,避免了误差的累积,大大减少了不确定性的传递,且计算效率较高。     

空间归一化边界识别方法用于判断地质体的水平位置及深度(英文)

边界识别是重磁数据解释中的常用方法之一,依据其结果可划分出地质体的水平范围。边界识别结果受地质体埋深及导数计算误差的影响所识别边界与真实边界之间存在一定的差距,且边界识别法无法直观地给出地质体的深度信息。为了获得异常体的水平位置和深度信息,本文提出空间归一化边界识别方法,其对不同深度的边界识别函数进行归一化计算,空间归一化边界识别法的最大值对应于异常体的水平位置和深度。常规边界识别结果的误差随理深的减小而减小,而空间归一化边界识别法是通过最大值来判断地质体的位置,最大值是在地质体处获得,因此归一化边界识别方法所获得的结果是准确的。通过理论模型试验证明归一化边界识别方法能有效地完成异常体的水平位置和深度的计算,所获得的水平位置和深度信息与理论值相一致,为下一步的勘探计划提供了更加可靠的依据。将其应用于实际航磁数据的解释,获得了断裂的具体分布形式。     

The four dimensional variational data assimilation with multiple regularization parameters as a weak constraint (Tikh-4D-Var) and its preliminary application on typhoon initialization

Traditional variational data assimilation (VDA) with only one regularization parameter constraint cannot produce optimal error tuning for all observations. In this paper, a new data assimilation method of “four dimensional variational data assimilation (4D-Var) with multiple regularization parameters as a weak constraint (Tikh-4D-Var)” is proposed by imposing different regularization parameters for different observations. Meanwhile, a new multiple regularization parameters selection method, which is suitable for actual high-dimensional data assimilation system, is proposed based on the posterior information of 4D-Var system. Compared with the traditional single regularization parameter selection method, computation of the proposed multiple regularization parameters selection method is smaller. Based on WRF3.3.1 4D-Var data assimilation system, initialization and simulation of typhoon Chaba (2010) with the new Tikh-4D-Var method are compared with its counterpart 4D-Var to demonstrate the effectiveness of the new method. Results show that the new Tikh-4D-Var method can accelerate the convergence with less iterations. Moreover, compared with 4D-Var method, the typhoon track, intensity (including center surface pressure and maximum wind speed) and structure prediction are obviously improved with Tikh-4D-Var method for 72-h prediction. In addition, the accuracy of the observation error variances can be reflected by the multiple regularization parameters.     

基于贝叶斯理论的逐次迭代非线性AVA反演方法

常规AVA三参数反演方法均基于横波速度与纵波速度之比γ为常数这一假设条件,且常被近似地取为0.5。然而在许多情况下γ并不为常数,而是在横向与纵向都渐变。若一概假定γ等于0.5,反演出的岩性参数势必要偏离真实值,因此有必要合理地选择γ。笔者基于贝叶斯理论,提出逐次迭代非线性AVA的反演方法。该方法把γ看成横向与纵向都渐变的反演初始背景,通过给定初始模型计算初始背景γ,并采用逐次迭代的策略求解该反演问题,解决了关于γ的选取问题以及由于引入变γ值而带来的非线性问题,提高了AVA三参数反演结果的精确度。     

融合多源重力数据的Tikhonov正则化配置法

针对最小二乘配置法在融合处理多源重力数据过程中可能出现的病态性问题,特别引入Tikhonov正则化方法,对配置法计算模型进行正则化改造,建立了相应的正则化配置模型。使用EGM2008位模型模拟产生航空重力和海面船测重力数据进行了融合处理仿真实验,实际验证了正则化处理方法的有效性。     

A multi‐stock,length‐based assessment model for New Zealand rock lobster (Jasus Edwardsii)

We describe a multi‐stock, length‐based Bayesian assessment model for New Zealand spiny lobster (Jasus edwardsii) fisheries. This model allows simultaneous modelling of two or more stocks with a mixture of common and stock‐specific parameters: recruitment is always stock‐specific, but any other parameter can be specified as either common or stock‐specific. Common parameters are estimated from a wider base than they would be in a single‐stock model. The model's time step is flexible and can be changed during the period being modelled to accommodate better data quality in recent data. Other options include the capacity to estimate movements among stocks, allow density‐dependent growth, and choose among likelihood functions for the various data sets, between finite and instantaneous fishing dynamics and between two forms of selectivity curve.     

贝叶斯网络在水资源管理中的应用

为了解决水资源管理中具有不确定性的多目标决策问题,将贝叶斯网络方法引入水资源管理中。通过对实例系统中变量间相互关系的分析,构建描述变量间不确定性关系的贝叶斯网络模型,其中包括表示其依赖关系的有向无环图和表示其具体概率依赖程度的条件概率表,并在6个目标变量均达到预期目标的前提下进行概率推理。实例结果表明：当补偿款数额增加到500元/亩时,所有的目标变量均可达到最优,因此确定出政府应给农民补偿款的数额为500元/亩的合理水资源决策方案。贝叶斯网络以图模型的方式直观地表达了实例系统中变量之间的不确定性关系,概率推理的结果兼顾了环境效益以及农民的利益,使多个预期目标均达到了最优,有效地解决了水资源管理中具有不确定性的多目标决策问题。     

协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测

精确估算森林蓄积量是国家实现2060年前碳中和目标的迫切需求,而基于遥感的森林蓄积量定量反演是当前遥感应用领域面临的重要挑战和研究热点.光学遥感数据由于无法获取森林高度信息并存在信号饱和问题,反演森林蓄积量的精度较低,而机载Lidar数据能获取高度信息,但成本高、观测范围有限.本研究利用Sentinel-2多光谱、资源...