globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-3351-2018
论文题名:
Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods
作者: Barfod A.A.S.; Møller I.; Christiansen A.V.; Hoyer A.-S.; Hoffimann J.; Straubhaar J.; Caers J.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:6
起始页码: 3351
结束页码: 3373
语种: 英语
Scopus关键词: Groundwater ; Stochastic systems ; Surveys ; Transients ; Trees (mathematics) ; Uncertainty analysis ; Degree of flexibility ; Geostatistical modeling ; Groundwater models ; Modeling procedure ; Multiple-point statistics ; Parameter setting ; Transient electromagnetic methods ; Visual inspection ; Stochastic models ; airborne sensing ; computer simulation ; data set ; electromagnetic method ; geophysical survey ; groundwater ; parameterization ; statistical analysis ; stochasticity ; stratigraphy ; visualization ; Denmark
英文摘要: Creating increasingly realistic groundwater models involves the inclusion of additional geological and geophysical data in the hydrostratigraphic modeling procedure. Using multiple-point statistics (MPS) for stochastic hydrostratigraphic modeling provides a degree of flexibility that allows the incorporation of elaborate datasets and provides a framework for stochastic hydrostratigraphic modeling. This paper focuses on comparing three MPS methods: snesim, DS and iqsim. The MPS methods are tested and compared on a real-world hydrogeophysical survey from Kasted in Denmark, which covers an area of 45ĝ€km2. A controlled test environment, similar to a synthetic test case, is constructed from the Kasted survey and is used to compare the modeling results of the three aforementioned MPS methods. The comparison of the stochastic hydrostratigraphic MPS models is carried out in an elaborate scheme of visual inspection, mathematical similarity and consistency with boreholes. Using the Kasted survey data, an example for modeling new survey areas is presented. A cognitive hydrostratigraphic model of one area is used as a training image (TI) to create a suite of stochastic hydrostratigraphic models in a new survey area. The advantage of stochastic modeling is that detailed multiple point information from one area can be easily transferred to another area considering uncertainty.

The presented MPS methods each have their own set of advantages and disadvantages. The DS method had average computation times of 6-7ĝ€h, which is large, compared to iqsim with average computation times of 10-12ĝ€min. However, iqsim generally did not properly constrain the near-surface part of the spatially dense soft data variable. The computation time of 2-3ĝ€h for snesim was in between DS and iqsim. The snesim implementation used here is part of the Stanford Geostatistical Modeling Software, or SGeMS. The snesim setup was not trivial, with numerous parameter settings, usage of multiple grids and a search-tree database. However, once the parameters had been set it yielded comparable results to the other methods. Both iqsim and DS are easy to script and run in parallel on a server, which is not the case for the snesim implementation in SGeMS. © 2018 Author(s).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163281
Appears in Collections:气候变化与战略

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作者单位: Barfod, A.A.S., Department of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, Aarhus C, 8000, Denmark, Hydrogeophysics Group, Department of Geoscience, Aarhus University, C.F. Møllers Allé 4, Aarhus C, 8000, Denmark; Møller, I., Department of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, Aarhus C, 8000, Denmark; Christiansen, A.V., Hydrogeophysics Group, Department of Geoscience, Aarhus University, C.F. Møllers Allé 4, Aarhus C, 8000, Denmark; Hoyer, A.-S., Department of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, Aarhus C, 8000, Denmark; Hoffimann, J., Stanford Center for Reservoir Forecasting, School of Earth, Energy and Environmental Sciences, Stanford University, Green Earth Sciences, 367 Panama St, Stanford, CA 94305, United States; Straubhaar, J., Centre d'Hydrogéologie et de Géothermie (CHYN), Université de Neuchâtel, Switzerland; Caers, J., Stanford Center for Reservoir Forecasting, School of Earth, Energy and Environmental Sciences, Stanford University, Green Earth Sciences, 367 Panama St, Stanford, CA 94305, United States

Recommended Citation:
Barfod A.A.S.,Møller I.,Christiansen A.V.,et al. Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods[J]. Hydrology and Earth System Sciences,2018-01-01,22(6)
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