Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR020299 |
Large-scale inverse model analyses employing fast randomized data reduction | |
Lin, Youzuo1; 39;Malley, Daniel2 | |
2017-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:8 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 10(7) or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called sketching matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity. |
英文关键词 | hydraulic inverse modeling data reduction randomization geostatistical inversion |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000411202000025 |
WOS关键词 | 3-DIMENSIONAL NUMERICAL INVERSION ; COMPONENT GEOSTATISTICAL APPROACH ; LEVENBERG-MARQUARDT ALGORITHM ; CROSS-HOLE TESTS ; HYDRAULIC TOMOGRAPHY ; STATISTICAL APPROACH ; AQUIFER HYDROLOGY ; TEMPORAL MOMENTS ; MATRICES ; PARAMETERS |
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/20819 |
专题 | 资源环境科学 |
作者单位 | 1.Los Alamos Natl Lab, Earth & Environm Sci Div, Los Alamos, NM USA; 2.Univ Texas Austin, Inst Computat Sci & Engn, Austin, TX 78712 USA |
推荐引用方式 GB/T 7714 | Lin, Youzuo,39;Malley, Daniel. Large-scale inverse model analyses employing fast randomized data reduction[J]. WATER RESOURCES RESEARCH,2017,53(8). |
APA | Lin, Youzuo,&39;Malley, Daniel.(2017).Large-scale inverse model analyses employing fast randomized data reduction.WATER RESOURCES RESEARCH,53(8). |
MLA | Lin, Youzuo,et al."Large-scale inverse model analyses employing fast randomized data reduction".WATER RESOURCES RESEARCH 53.8(2017). |
条目包含的文件 | 条目无相关文件。 |
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