Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024840 |
Hydrogeological Model Selection Among Complex Spatial Priors | |
Brunetti, C.1; Bianchi, M.2; Pirot, G.1; Linde, N.1 | |
2019-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH
![]() |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:8页码:6729-6753 |
文章类型 | Article |
语种 | 英语 |
国家 | Switzerland; England |
英文摘要 | Hydrogeological field studies rely often on a single conceptual representation of the subsurface. This is problematic since the impact of a poorly chosen conceptual model on predictions might be significantly larger than the one caused by parameter uncertainty. Furthermore, conceptual models often need to incorporate geological concepts and patterns in order to provide meaningful uncertainty quantification and predictions. Consequently, several geologically realistic conceptual models should ideally be considered and evaluated in terms of their relative merits. Here, we propose a full Bayesian methodology based on Markov chain Monte Carlo to enable model selection among 2-D conceptual models that are sampled using training images and concepts from multiple-point statistics. More precisely, power posteriors for the different conceptual subsurface models are sampled using sequential geostatistical resampling and Graph Cuts. To demonstrate the methodology, we compare and rank five alternative conceptual geological models that have been proposed in the literature to describe aquifer heterogeneity at the MAcroDispersion Experiment site in Mississippi, USA. We consider a small-scale tracer test for which the spatial distribution of hydraulic conductivity impacts multilevel solute concentration data observed along a 2-D transect. The thermodynamic integration and the stepping-stone sampling methods were used to compute the evidence and associated Bayes factors using the computed power posteriors. We find that both methods are compatible with multiple-point statistics-based inversions and provide a consistent ranking of the competing conceptual models considered. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000490973700022 |
WOS关键词 | MARGINAL LIKELIHOOD ESTIMATION ; FLUVIOGLACIAL AQUIFER ANALOG ; GEOLOGICAL CROSS-SECTION ; ESTIMATING BAYES FACTORS ; CHAIN MONTE-CARLO ; MACRODISPERSION EXPERIMENT ; SOLUTE TRANSPORT ; MASS-TRANSFER ; THERMODYNAMIC INTEGRATION ; HETEROGENEOUS AQUIFER |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/185862 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Lausanne, Inst Earth Sci, Appl & Environm Geophys Grp, Lausanne, Switzerland; 2.British Geol Survey, Environm Sci Ctr, Nottingham, England |
推荐引用方式 GB/T 7714 | Brunetti, C.,Bianchi, M.,Pirot, G.,et al. Hydrogeological Model Selection Among Complex Spatial Priors[J]. WATER RESOURCES RESEARCH,2019,55(8):6729-6753. |
APA | Brunetti, C.,Bianchi, M.,Pirot, G.,&Linde, N..(2019).Hydrogeological Model Selection Among Complex Spatial Priors.WATER RESOURCES RESEARCH,55(8),6729-6753. |
MLA | Brunetti, C.,et al."Hydrogeological Model Selection Among Complex Spatial Priors".WATER RESOURCES RESEARCH 55.8(2019):6729-6753. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论