大兴安岭诺敏河火山群远震P波衰减研究

板内火山活动是板内下方深部物质组成的重要视窗,而中国东北是研究板内火山的热点场所.中国东北火山活动主要沿大兴安岭及松辽盆地周缘分布,隐匿在大兴安岭内的各火山群落鲜有地球物理观测.本研究聚焦于大兴安岭北部的诺敏河火山群,利用围绕诺敏河火山布设的43个流动台站记录到的17个远震事件数据,通过时间域波形匹配法计算了300条直达P波震相的Δt~*,并进一步采用贝叶斯蒙特卡洛方法反演得到了研究区的二维Δt~*模型,为约束诺敏河火山地区的深部结构提供了新的观测证据.结果显示研究区高衰减区域与火山活动在空间位置上有很好的一致性.诺敏河火山与研究区北端临近小古里河火山区域均观测到高Δt~*值,可能由区域下方热地幔物质上涌导致.诺敏河火山东部、科洛河火山西部区域观测到低Δt~*值,与高衰减区域最大Δt~*差值达约0.3±0.05 s,可能由两部分原因导致:该区域下方残留未被侵蚀的岩石圈,以及局部区域或存在的部分熔融导致的残留地幔矿物脱水.     

Comparative study of the effects of three data-interpretation methodologies on the performance of geotechnical back analysis

Back analysis can provide engineers with important information for better decision-making. Over the years, research on back analysis has focused mainly on optimisation techniques, while comparative studies of data-interpretation methodologies have seldom been reported. This paper examines the use of three data-interpretation methodologies on the performance of geotechnical back analysis. In general, there are two types of approaches for interpreting model predictions using field measurements, deterministic versus population-based, both of which are considered in this study. The methodologies that are compared are (a) error-domain model falsification (EDMF), (b) Bayesian model updating and (c) residual minimisation. Back analyses of an excavation case history in Singapore using the three methodologies indicate that each has strengths and limitations. Residual minimisation, though easy to implement, shows limited capabilities of interpreting measurement data with large uncertainty errors. EDMF provides robustness against incomplete information of the correlation structure. This is achieved at the expense of precision, as EDMF yields wider confidence intervals of the identified parameter values and predicted quantities compared with Bayesian model updating. In this regard, a modified EDMF implementation is proposed, which can improve upon the limitations of the traditional EDMF method, thus enhancing the quality of the identification outcomes.     

病态总体最小二乘问题的广义正则化

总体最小二乘(TLS)算法可以视为一个降正则化的过程,对比最小二乘算法,病态总体最小二乘方法的解受系数阵数据误差和观测值误差的影响将更为严重。本文探讨用广义正则化的方法降低病态性对总体最小二乘数值求解的影响,以提高求解结果的稳定性。通过多组算例结果表明,本文采用的广义正则化方法在处理病态总体最小二乘问题上具有明显的优势。     

Bayesian modelling of environmental risk: example using a small area ecological study of coronary heart disease mortality in relation to modelled outdoor nitrogen oxide levels

Bayesian modelling of health risks in relation to environmental exposures offers advantages over conventional (non-Bayesian) modelling approaches. We report an example using research into whether, after controlling for different confounders, air pollution (NO_x) has a significant effect on coronary heart disease mortality, estimating the relative risk associated with different levels of exposure. We use small area data from Sheffield, England and describe how the data were assembled. We compare the results obtained using a generalized (Poisson) log-linear model with adjustment for overdispersion, with the results obtained using a hierarchical (Poisson) log-linear model with spatial random effects. Both classes of models were fitted using a Bayesian approach. Including spatial random effects models both overdispersion and spatial autocorrelation effects arising as a result of analysing data from small contiguous areas. The first modelling framework has been widely used, while the second provides a more rigorous model for hypothesis testing and risk estimation when data refer to small areas. When the models are fitted controlling only for the age and sex of the populations, the generalized log-linear model shows NO_x effects are significant at all levels, whereas the hierarchical log-linear model with spatial random effects shows significant effects only at higher levels. We then adjust for deprivation and smoking prevalence. Uncertainty in the estimates of smoking prevalence, arising because the data are based on samples, was accounted for through errors-in-variables modelling. NO_x effects apparently are significant at the two highest levels according to both modelling frameworks.

Bursa模型用于局部区域坐标变换的病态问题及其解法

GPS应用经常涉及坐标变换。用局部区域的GPS网数据求解的3维坐标变换模型的转换参数时,求得的转换参数特别是平移参数的精度较差。这是由于GPS网的范围太小,引起平移参数与旋转参数间存在强相关性,导致解算模型病态。正则化解法是求解病态方程的有效工具,本文探讨用正则化方法解算小范围GPS网3维坐标变换的转换参数,以提高转换参数的解算精度,扩大参数的使用范围;给出只对平移参数进行正则化的计算模型。500次模拟计算结果表明:正则化解参数转换外围点坐标的精度在统计上明显优于最小二乘解;且随外推距离增大,精度几乎成线性降低。     

Theory for Reconstruction of an Unknown Number of Contaminant Sources using Probabilistic Inference

We address the inverse problem of source reconstruction for the difficult case of multiple sources when the number of sources is unknown a priori. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to derive the posterior probability density function for the number of sources and for the parameters (e.g., location, emission rate, release time and duration) that characterize each source. A mapping (source–receptor relationship) that relates a multiple source distribution to the concentration measurements made by an array of detectors is formulated based on a forward-time Lagrangian stochastic model. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source–receptor relationship and realized in terms of a backward-time Lagrangian stochastic model, is described. An efficient computational algorithm based on a parallel tempered Metropolis-coupled reversible-jump Markov chain Monte Carlo (MCMC) method is formulated and implemented to draw samples from the posterior probability density function of the source parameters. This methodology allows the MCMC method to initiate jumps between the hypothesis spaces corresponding to different numbers of sources in the source distribution and, thereby, allows a sample from the full joint posterior distribution of the number of sources and the parameters for each source to be obtained. The proposed methodology for source reconstruction is tested using synthetic concentration data generated for cases involving two and three unknown sources.     

Climate,landowner residency,and land cover predict local scale fire activity in the Western Amazon

The incidence of escaped agricultural fire has recently been increasing in the Western Amazon, driven by climate variability, land use change, and changes in patterns of residency and land occupation. Preventing and mitigating the negative impacts of fire in the Amazon require a comprehensive understanding not only of what the drivers of fire activity are, but also how these drivers interact and vary across scales. Here, we combine multi-scalar data on land use, climate, and landowner residency to disentangle the drivers of fire activity over 10 years (2001–2010) on individual landholdings in a fire-prone region of the Peruvian Amazon. We examined the relative importance of and interactions between climate variability (drought intensity), land occupation (in particular, landowner absenteeism), and land cover variables (cover of fallow and pasture) for predicting both fire occurrence (whether or not fire was detected on a farm in a given year) and fire size. Drought intensity was the most important predictor of fire occurrence, but land-cover type and degree of landowner absenteeism increased fire probability when conditions were dry enough. On the other hand, drought intensity did not stand out relative to other significant predictors in the fire size model, where degree of landowner absenteeism in a village and percent cover of fallow in a village were also strongly associated with fire size. We also investigated to what extent these variables measured at the individual landholding versus the village scale influenced fire activity. While the predictors measured at the landholding and village scales were approximately of equal importance for modeling fire occurrence, only village scale predictors were important in the model of fire size. These results demonstrate that the relative importance of various drivers of fire activity can vary depending on the scale at which they are measured and the scale of analysis. Additionally, we highlight how a full understanding of the drivers of fire activity should go beyond fire occurrence to consider other metrics of fire activity such as fire size, as implications for fire prevention and mitigation can be different depending on the model considered. Drought early warning systems may be most effective for preventing fire in dry years, but management to address the impacts of landowner absenteeism, such as bolstering community fire control efforts in high-risk areas, could help minimize the size of fires when they do occur. Thus, interventions should focus on minimizing fire size as well as preventing fires altogether, especially because fire is an inexpensive and effective management tool that has been in use for millennia.     

Plant source water apportionment using stable isotopes: A comparison of simple linear,two‐compartment mixing model approaches

Plant source water identification using stable isotopes is now a common practice in ecohydrological process investigations. Notwithstanding, little critical evaluation of the approaches for source apportionment have been conducted. Here, we present a critical evaluation of the main methods used for source apportionment between vadose and saturated zone water: simple mass balance and Bayesian mixing models. We leverage new isotope stem water samples from a diverse set of tree species in a strikingly uniform terrain and soil conditions at the Christchurch Botanic Garden, New Zealand. Our results show that using δ²H alone in a simple, two‐source mass balance approach leads to erroneous results, particularly an apparent overestimation of groundwater contribution to xylem. Alternatively, using both δ²H and δ¹⁸O in a Bayesian inference framework improves the source water estimates and is more useful than the simple mass balance approach, particularly when soil and groundwater contributions are relatively disproportionate. We suggest that plant source water quantification methods should take into consideration the possible effects of ²H/¹H fractionation. The Bayesian inference approach used here may be less sensitive to ²H/¹H fractionation effects than simple mass balance methods.     

Suspended sediment source apportionment in Chesapeake Bay watershed using Bayesian chemical mass balance receptor modeling

A Bayesian chemical mass balance approach was used to estimate the contributions of potential sources into the suspended sediments in the Mill Stream, a tributary of Chesapeake Bay in Maryland, US. The Bayesian approach allows us to incorporate the uncertainties as a result of measurement errors in the elemental profiles of sources and suspended sediments in the stream and also the heterogeneities of the elemental profiles among samples into the analysis and provides us with probability densities representing the contribution of each source. Based on the obtained results, river bank erosion is the major source of sediments with a contribution estimated to range from 83% to 99% with a 95% credible interval. Also, a 95% credible interval in a range of 0.06% to 8% for cropland and 0.1% to 14% for forestland was provided. Hence, while the contribution of sediments from cropland or forestland cannot be ruled out, the model does not definitively demonstrate a significant contribution from either source. Copyright © 2012 John Wiley & Sons, Ltd.