Resampling from stochastic simulations

To model the uncertainty of an estimate of a global property, the estimation process is repeated on multiple simulated fields, with the same sampling strategy and estimation algorithm. As opposed to conventional bootstrap, this resampling scheme allows for spatially correlated data and the common situation of preferential and biased sampling. The practice of this technique is developed on a large data set where the reference sampling distributions are available. Comparison of the resampled distributions to that reference shows the probability intervals obtained by resampling to be reasonably accurate and conservative, provided the original and actual sample has been corrected for the major biases induced by preferential sampling.Andre G. Journel is a Professor of Petroleum Engineering at Stanford University with a joint appointment in the Department of Geological and Environmental Sciences. He is, also, Director of the Stanford Center for Reservoir Forecasting. Professor Journel has pioneered applications of geostatistical techniques in the mining/petroleum industry and extended his expertise to environmental applications and repository site characterization. Most notably, he developed the concept of non-parametric geostatistics and stochastic imaging with application to modeling uncertainty in reservoir/site characterization. Although the research described in this article has been supported by the United States Environmental Protection Agency under Cooperative Agreement CR819407, it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

基于条件高斯模拟的城市灰尘Pb元素的空间分布特征

城市灰尘是分散于城市不同区域的表面固体颗粒物。城市灰尘对城市环境造成的污染越来越多地受到人们的关注,尤其是城市灰尘重金属对城市生存环境造成的危害。本文利用R软件中的Gstat包,对金华市城市地表灰尘中的Pb元素含量进行500次条件高斯模拟,并从中随机抽出3次模拟对其模拟结果进行分析,从模拟结果可以看出研究区Pb元素总体上显示出相似的空间变化趋势,但在细部空间变化上是有差别的。条件高斯模拟法对未知点的模拟值具有不确定性,克服了普通克里格法的平滑效应,能够更好地反映研究区的随机性和结构性。     

The relevance of James Lovelock’s research and philosophy to environmental science and academia

James E. Lovelock, famed for his Gaia hypothesis, which views the Earth as a living integrated and interconnected self-regulating system whose equilibrium comes about from complex energy-based interactions and feedback loops, ultimately sustaining life, passed away at the end of July, 2022 at the age of 103. Not only are the adaptive mechanisms of Gaia central to the conversation of environmental homeostasis, they lie at the heart of climate change and global warming. Lovelock is also remembered as the co-inventor of the electron capture detector that eventually allowed for the sensitive detection of chlorofluorocarbons and pesticides. Finally, Lovelock’s free-spirited nature and research independence allow academia to rethink current research’s modus operandi.     

镉、汞、铅和石油烃复合污染对渤海湾常见渔业资源生物的影响初探(Effects of the Combined Pollution of Cd, Hg, Pb and Petroleum Hydrocarbons on Common Fishery Species in the Bohai Bay)

通过对1996～2005年渤海湾近岸海域海水镉、汞、铅和石油烃浓度变化的分析,发现海水中镉浓度呈明显上升趋势,汞、铅和石油烃则无明显变化趋势.加速生命试验法模型(Accelerated Life Testing model)的研究显示,1996～2005年渤海湾近岸海域海水镉、汞和铅平均浓度均已超过其对渤海湾常见渔业资源生物的安全浓度.镉、汞、铅和石油烃对生物的长期致死率鱼类分别为4.5%、16.3%、0.0%和12.0%,甲壳类为0.4%、7.9%、0.3%和6.6%,双壳类为10.5%、0.2%、0.2%和2.3%.效应加和模型(Independence Action model)的估算表明,在镉、汞、铅和石油烃组成的复合污染条件下,渤海湾常见鱼类、甲壳类和双壳类的长期死亡率分别为29.7%、14.6%和12.9%,其种群增长率分别降低约6.4%、14.6%和12.9%.与镉、汞、铅和石油烃单种污染物暴露相比,其复合污染导致的渤海湾常见渔业资源生物种群(鱼类、甲壳类和双壳类)增长率的降低更明显.因此,复合污染是导致渤海湾渔业资源衰退的重要因素。     

基于技术扩散的全社会能源效率空间条件收敛分析

我国各地区能源效率存在明显的差距,如何缩小地区能源效率差距成为了能源经济领域的热点问题.本文依据2000-2010年中国30省面板数据,运用空间误差条件β收敛模型分析了技术扩散对全社会能源效率收敛的影响,研究结果表明:①2000-2010年间,全社会能源效率呈现2000-2002、2002-2005和2005-2010的三阶段特征,经历上升、下降、再上升的变化过程,其中,2005-2010年提升速度最快,其经验值得借鉴;②全社会能源效率的绝对β收敛呈现由收敛至发散的三阶段变化过程;③全社会能源效率存在显著的空间滞后现象,其增长率的空间相依性不显著,但空间因素对全社会能源效率β收敛的正面影响有加强的趋势;④技术扩散从总体上使全社会能源效率的收敛速度提高1.8倍以上,且影响程度逐渐增强;⑤外商直接投资有助于提升全社会能源效率及其收敛速度;⑥不同时段的技术扩散对全社会能源效率的影响不同,且技术引进和国内技术市场技术流入对收敛的作用不显著.因此,引进外资是缩小地区间全社会能源效率差距的重要措施.     

保护层分析中独立保护层的识别研究

为阐述保护层分析(LOPA)中独立保护层(IPL)的识别规则,以及这些规则在实际应用中要注意的问题,以生产聚氯乙烯(PVC)的间歇聚合反应为例,对8个不同的LOPA场景进行分析,给出不同场景的IPL和要求时的失效概率(PFD),以及建议增加的IPL。分析结果表明,在进行IPL的识别时,应重点确认IPL的有效性和独立性。在评估IPL有效性时,应关注具有共同元件的IPL,IPL的行动能力、人员行动有效性及IPL的PFD等。在评估独立性时,应确保IPL独立于初始事件和同一场景中的其他IPL的任何构成元件。通过分析,发现PVC工艺中安全阀(PSV)设计、安全仪表功能(SIF)设计和人员行动等IPL中存在的问题,并提出相应的建议。     

Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin,Ethiopia1

Abstract: This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain‐fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long‐term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100‐year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be ?7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.     

中国农业碳排放的空间分异与动态演进——基于空间和非参数估计方法的实证研究

为研究农业碳排放的空间分布与动态演进规律，运用1993~2017年中国大陆29个省份的面板数据，在分省测算农业碳排放量和农业碳排放强度的基础上，将空间和非参数估计方法相结合，考察了中国农业碳排放的空间分异及其动态演进.结果表明：中国农业碳排放量呈“中心-外围”模式分布，且表现出向“外围”扩散的趋势；碳排放强度呈逐渐下降的趋势，表现为西高东低的阶梯状分布特征；碳排放的地区间差距不断缩小，农业碳排放的增长分布由“二主一小”向“一主”的格局转变，与此同时，中国农业碳排放还表现出由“两极”向“多极”转变的分布格局.     

基于贝叶斯网的交通事故机理分析

针对道路交通事故的形成机理进行定性、定量研究,根据我国道路交通事故记录数据特征,应用贝叶斯网对事故发生概率进行定量分析.引入"驾驶员紧张度"和"道路线形合理度"两个隐节点,建立了事故分析的贝叶斯网多层隐类模型,采用最大似然估计方法确定了模型的边缘概率和条件概率.将贝叶斯网模型应用于国道104二级公路(K1310+000～K1330+000)的事故分析中,运用贝叶斯网分析软件包Netica对其历史事故记录数据进行分析.结果表明: 贝叶斯网不仅可以定量计算某种道路交通状态下的事故发生概率,而且可以找出影响事故概率的关键原因和最不利状态组合(事故概率最大时的道路交通状态).     

A single-chain-based multidimensional Markov chain model for subsurface characterization

Multidimensional Markov chain models in geosciences were often built on multiple chains, one in each direction, and assumed these 1-D chains to be independent of each other. Thus, unwanted transitions (i.e., transitions of multiple chains to the same location with unequal states) inevitably occur and have to be excluded in estimating the states at unobserved locations. This consequently may result in unreliable estimates, such as underestimation of small classes (i.e., classes with smaller than average areas) in simulated realizations. This paper presents a single-chain-based multidimensional Markov chain model for estimation (i.e., prediction and conditional stochastic simulation) of spatial distribution of subsurface formations with borehole data. The model assumes that a single Markov chain moves in a lattice space, interacting with its nearest known neighbors through different transition probability rules in different cardinal directions. The conditional probability distribution of the Markov chain at the location to be estimated is formulated in an explicit form by following the Bayes’ Theorem and the conditional independence of sparse data in cardinal directions. Since no unwanted transitions are involved, the model can estimate all classes fairly. Transiogram models (i.e., 1-D continuous Markov transition probability diagrams) are used to provide transition probability input with needed lags to generalize the model. Therefore, conditional simulation can be conducted directly and efficiently. The model provides an alternative for heterogeneity characterization of subsurface formations.