Articles | Volume 22, issue 6
https://doi.org/10.5194/hess-22-3351-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-3351-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jun 2018
Research article |
| 18 Jun 2018
| 18 Jun 2018
Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods
Adrian A. S. Barfod
CORRESPONDING AUTHOR
Department of Groundwater and Quaternary Geology Mapping, Geological
Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, 8000
Aarhus C, Denmark
Hydrogeophysics Group, Department of Geoscience, Aarhus University,
C.F. Møllers Allé 4, 8000 Aarhus C, Denmark
Ingelise Møller
Department of Groundwater and Quaternary Geology Mapping, Geological
Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, 8000
Aarhus C, Denmark
Anders V. Christiansen
Hydrogeophysics Group, Department of Geoscience, Aarhus University,
C.F. Møllers Allé 4, 8000 Aarhus C, Denmark
Anne-Sophie Høyer
Department of Groundwater and Quaternary Geology Mapping, Geological
Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, 8000
Aarhus C, Denmark
Júlio Hoffimann
Stanford Center for Reservoir Forecasting, School of Earth, Energy
& Environmental Sciences, Stanford University, Green Earth Sciences, 367
Panama St, Stanford, CA 94305, USA
Julien Straubhaar
Centre d'Hydrogéologie et de Géothermie (CHYN), Université
de Neuchâtel, Switzerland
Jef Caers
Stanford Center for Reservoir Forecasting, School of Earth, Energy
& Environmental Sciences, Stanford University, Green Earth Sciences, 367
Panama St, Stanford, CA 94305, USA
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25 citations as recorded by crossref.
- Multiple-point statistical modeling of three-dimensional glacial aquifer heterogeneity for improved groundwater management N. Kawo et al. 10.1007/s10040-023-02658-x
- AEM in Norway: A Review of the Coverage, Applications and the State of Technology E. Harrison et al. 10.3390/rs13224687
- Exploring the Model Space of Airborne Electromagnetic Data to Delineate Large‐Scale Structure and Heterogeneity Within an Aquifer System S. Kang et al. 10.1029/2021WR029699
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- Hybrid Seismic-Electrical Data Acquisition Station Based on Cloud Technology and Green IoT S. Qiao et al. 10.1109/ACCESS.2020.2966510
- Choosing between Gaussian and MPS simulation: the role of data information content—a case study using uncertain interpretation data points R. Madsen et al. 10.1007/s00477-020-01954-2
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- Combining Hydraulic Head Analysis with Airborne Electromagnetics to Detect and Map Impermeable Aquifer Boundaries J. Korus 10.3390/w10080975
- A Novel Methodology for the Stochastic Integration of Geophysical and Hydrogeological Data in Geologically Consistent Models A. Neven & P. Renard 10.1029/2023WR034992
- 3D multiple-point statistics simulations of the Roussillon Continental Pliocene aquifer using DeeSse V. Dall'Alba et al. 10.5194/hess-24-4997-2020
- The influence of layer and voxel geological modelling strategy on groundwater modelling results T. Enemark et al. 10.1007/s10040-021-02442-9
- Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) Z. Yin et al. 10.5194/gmd-13-651-2020
- Improved Imaging of the Large‐Scale Structure of a Groundwater System With Airborne Electromagnetic Data S. Kang et al. 10.1029/2021WR031439
- Stochastic Reconstruction of 3D Heterogeneous Microstructure Using a Column-Oriented Multiple-Point Statistics Program C. Zuo et al. 10.2113/2024/lithosphere_2023_233
- 3D Geological Image Synthesis From 2D Examples Using Generative Adversarial Networks G. Coiffier et al. 10.3389/frwa.2020.560598
- Restoring groundwater levels after tunneling: a numerical simulation approach to tunnel sealing decision-making M. Golian et al. 10.1007/s10040-021-02315-1
- Grounded electrical source ground–airborne transient electromagnetic modelling with fictitious wave field methods X. Meng et al. 10.1007/s12040-020-01391-3
- A nearest neighbor multiple-point statistics method for fast geological modeling C. Zuo et al. 10.1016/j.cageo.2022.105208
- Double verification and quantitative traceability: A solution for mixed mine water sources Y. Zeng et al. 10.1016/j.jhydrol.2024.130725
- Integration of Soft Data Into Geostatistical Simulation of Categorical Variables S. Carle & G. Fogg 10.3389/feart.2020.565707
- Assessment of Managed Aquifer Recharge Sites Using a New Geophysical Imaging Method A. Behroozmand et al. 10.2136/vzj2018.10.0184
- Entropy and Information Content of Geostatistical Models T. Hansen 10.1007/s11004-020-09876-z
- From legacy contamination to watershed systems science: a review of scientific insights and technologies developed through DOE-supported research in water and energy security D. Dwivedi et al. 10.1088/1748-9326/ac59a9
- Stochastic multi-fidelity joint hydrogeophysical inversion of consistent geological models A. Neven et al. 10.3389/frwa.2022.989440
- Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions H. Wainwright et al. 10.5194/hess-26-429-2022
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Latest update: 18 Apr 2024
Short summary
Three-dimensional geological models are important to securing and managing groundwater. Such models describe the geological architecture, which is used for modeling the flow of groundwater. Common geological modeling approaches result in one model, which does not quantify the architectural uncertainty of the geology.
We present a comparison of three different state-of-the-art stochastic multiple-point statistical methods for quantifying the geological uncertainty using real-world datasets.
Three-dimensional geological models are important to securing and managing groundwater. Such...
