利用限制性最大似然方法和贝叶斯方法估计大菱鲆生长性状的遗传参数。

Bayesian and restricted maximum likelihood(REML) approaches were used to estimate the genetic parameters in a cultured turbot Scophthalmus maximus stock. The data set consisted of harvest body weight from 2 462progenies(17 months old) from 28 families that were produced through artificial insemination using 39 parent fish. An animal model was applied to partition each weight value into a fixed effect, an additive genetic effect, and a residual effect. The average body weight of each family, which was measured at 110 days post-hatching, was considered as a covariate. For Bayesian analysis, heritability and breeding values were estimated using both the posterior mean and mode from the joint posterior conditional distribution. The results revealed that for additive genetic variance, the posterior mean estimate( δ_a~2=9 320) was highest but with the smallest residual variance,REML estimates( δ_a~2=8 088) came second and the posterior mode estimate( δ_a~2=7 849) was lowest. The corresponding three heritability estimates followed the same trend as additive genetic variance and they were all high. The Pearson correlations between each pair of the three estimates of breeding values were all high,particularly that between the posterior mean and REML estimates(0.996 9). These results reveal that the differences between Bayesian and REML methods in terms of estimation of heritability and breeding values were small. This study provides another feasible method of genetic parameter estimation in selective breeding programs of turbot.     

Learning soil parameters and updating geotechnical reliability estimates under spatial variability – theory and application to shallow foundations

ABSTRACT

Field data is commonly used to determine soil parameters for geotechnical analysis. Bayesian analysis allows combining field data with other information on soil parameters in a consistent manner. We show that the spatial variability of the soil properties and the associated measurements can be captured through two different modelling approaches. In the first approach, a single random variable (RV) represents the soil property within the area of interest, while the second approach models the spatial variability explicitly with a random field (RF). We apply the Bayesian concept exemplarily to the reliability assessment of a shallow foundation in a silty soil with spatially variable data. We show that the simpler RV approach is applicable in cases where the measurements do not influence the correlation structure of the soil property at the vicinity of the foundation. In other cases, it is expected to underestimate the reliability, and a RF model is required to obtain accurate results.     

基于数据规则化和稀疏反演的三维表面多次波压制方法

表面多次波是海洋地震勘探中的主要问题.目前,二维数据驱动的表面多次波压制技术(SRME)已经比较成熟,并且已经成为工业界压制海洋表面多次波的主流方法.但是由于二维SRME算法没有考虑横测线方向上多次波的贡献,导致在处理实际三维海洋资料时存在比较大的误差.将二维SRME算法扩展到三维空间后可以得到三维SRME算法,但是由于目前实际采集的三维海洋资料的观测系统存在拖缆漂移,而且横测线方向采样过于稀疏,直接应用三维SRME算法无法准确预测表面多次波.本文提出的通过数据规则化配合稀疏反演的三维表面多次波压制方法能够解决这种实际资料和三维SRME算法之间的矛盾.本文通过研究数据规则化与反规则化技术,使得数据分布满足三维SRME的要求;通过研究稀疏反演技术,有效解决了横测线方向采样稀疏对于多次波预测的影响,三维实际海洋资料的应用结果验证了方法的有效性和可行性.     

Improved performance‐based seismic assessment of buildings by utilizing Bayesian statistics

This paper addresses two important issues of concern to practicing engineers and researchers alike in application of performance‐based seismic assessment (PBSA) methodology on buildings: (i) the number of ground motion records required to exercise PBSA—current practice (FEMA P‐58‐1) requires eleven or more pairs of motions for this purpose, and (ii) the time and effort associated with performing the number of nonlinear response history analyses required to exercise PBSA. We present a method for exercising of PBSA that employs classical linear modal analysis to develop a first estimate (i.e., a priori) of probability distribution of loss, followed by utilizing Bayesian statistics to update this estimate using estimates of loss obtained by utilizing a small number of nonlinear response history analyses of a detailed model of the building (i.e., posterior). The proposed technique is used to assess the distribution of monetary loss of two case studies, a 4‐story reinforced concrete moment‐resisting frame building and a 20‐story steel moment‐resisting frame building, both located in Los Angeles, for a ground motion hazard with 10% probability of exceedance in 50 years. The efficiency of the proposed PBSA method is demonstrated by showing the similarity between the distribution of monetary loss at each story of case study buildings obtained from the traditional/sophisticated PBSA methodology and the proposed PBSA method in this study. Copyright © 2015 John Wiley & Sons, Ltd.     

位场各阶垂向导数换算的新正则化方法

位场垂向导数大量应用于位场数据处理与解释中.当前广泛采用的位场各阶垂向导数换算方法为基于Laplace方程并结合波数域和空间域方法的具有递推特性的ISVD(integrated second vertical derivative)算法.本文在位场垂向导数换算的正则化方法和径向平均功率谱的基础上,提出一种位场各阶垂向导数换算的新正则化方法.新正则化方法仅需通过分析位场径向平均功率谱来确定一个截止波数,即可稳定换算位场各阶垂向导数.理论模型和实测数据实验结果表明:(1)新正则化方法物理意义明确、计算简单,且各阶垂向导数换算的稳定性和精度明显优于ISVD算法;(2)在用新正则化方法求得各阶垂向导数的基础上,利用泰勒级数法可以获得大深度、高精度的位场向下延拓结果.     

储层重力密度反演后验约束正则化方法

本文针对蒸汽辅助重力泄油(SAGD)生产中开发监测问题,发展了综合应用地震及重力数据反演储层密度的联合反演算法.通过测井数据建立纵波阻抗与密度的直接关系,并推导出这种关系下重力与纵波阻抗数据联合反演的计算方法,从而计算出蒸汽腔体密度分布规律.文中应用密度反演后验约束正则化方法,采用Tikhonov正则化模型,通过波阻抗数据作为约束进行联合反演,在算法上提高了稳定性,同时得到较高的反演精度.文中对SAGD生产中的理论模型进行了方法试算,并分析了算法的误差,最终应用于SAGD生产的实际数据中,通过最终反演结果分析,该方法取得了很好的应用效果.     

L1范数约束被动源数据稀疏反演一次波估计

对于被动源地震数据,运用常规的互相关算法得到的虚拟炮记录中,不仅含有一次波反射信息,还包括了表面相关多次波.然而,通过传统的被动源数据稀疏反演一次波估计(EPSI)方法,可以求得只含有一次波,不含表面相关多次波的虚拟炮记录.本文改进了传统的被动源数据稀疏反演一次波估计问题的求解方法,将被动源稀疏反演一次波估计求解问题转化为双凸L1范数约束的最优化求解问题,避免了在传统的稀疏反演一次波估计过程中用时窗防止反演陷入局部最优化的情况.在L1范数约束最优化的求解过程中,又结合了2DCurvelet变换和小波变换,在2DCurvelet-wavelet域中,数据变得更加稀疏,从而使求得的结果更加准确,成像质量得到了改善.通过简单模型和复杂模型,验证了本文提出方法的有效性.     

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

In this paper, a Bayesian sequential sensor placement algorithm, based on the robust information entropy, is proposed for multi‐type of sensors. The presented methodology has two salient features. It is a holistic approach such that the overall performance of various types of sensors at different locations is assessed. Therefore, it provides a rational and effective strategy to design the sensor configuration, which optimizes the use of various available resources. This sequential algorithm is very efficient due to its Bayesian nature, in which prior distribution can be incorporated. Therefore, it avoids the possible unidentifiability problem encountered in a sequential process, which starts with small number of sensors. The proposed algorithm is demonstrated using a shear building and a lattice tower with consideration of up to four types of sensors. Copyright © 2014 John Wiley & Sons, Ltd.     

High‐temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

This contribution addresses two developing areas of sediment fingerprinting research. Specifically, how to improve the temporal resolution of source apportionment estimates whilst minimizing analytical costs and, secondly, how to consistently quantify all perceived uncertainties associated with the sediment mixing model procedure. This first matter is tackled by using direct X‐ray fluorescence spectroscopy (XRFS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analyses of suspended particulate matter (SPM) covered filter papers in conjunction with automatic water samplers. This method enables SPM geochemistry to be quickly, accurately, inexpensively and non‐destructively monitored at high‐temporal resolution throughout the progression of numerous precipitation events. We then employed a Bayesian mixing model procedure to provide full characterization of spatial geochemical variability, instrument precision and residual error to yield a realistic and coherent assessment of the uncertainties associated with source apportionment estimates. Applying these methods to SPM data from the River Wensum catchment, UK, we have been able to apportion, with uncertainty, sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources at 60‐ and 120‐minute resolution for the duration of five precipitation events. The results presented here demonstrate how combining Bayesian mixing models with the direct spectroscopic analysis of SPM‐covered filter papers can produce high‐temporal resolution source apportionment estimates that can assist with the appropriate targeting of sediment pollution mitigation measures at a catchment level. © 2015 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Anthropogenic land-use signals propagate through stream food webs in a California,USA, watershed

Human development of watersheds can change aquatic ecosystems via multiple pathways. For instance, human rural development may add nutrients to ecosystems. We used naturally occurring stable isotopes in stream food webs to investigate how land use affects stream ecosystems across a gradient of land development in the San Lorenzo watershed, California. Road density was used as a proxy for land development. We found that streams in watersheds with higher road densities had elevated concentrations of phosphate and nitrate. Furthermore, algal δ¹⁵N values increased as a function of nitrate concentration, but saturated at approximately 6‰. This saturating pattern was consistent with a two-source mixing model with anthropogenic and watershed sources, fit using Bayesian model fitting. In sites that had >2.6 km roads km⁻², anthropogenic sources of N were estimated to represent >90% of the N pool. This anthropogenic N signal was propagated to stream consumers: rainbow trout (Oncorhynchus mykiss), signal crayfish (Pacifasticus leniusculus), and benthic invertebrate δ¹⁵N were positively correlated with algal δ¹⁵N. Even relatively low density rural human land use may have substantial impacts on nutrient cycling of stream ecosystems.