41 citations as recorded by crossref.

1. Citizen science: A new perspective to advance spatial pattern evaluation in hydrology J. Koch et al. https://doi.org/10.1371/journal.pone.0178165
2. Three-dimensional voxel geological model of a riparian lowland and surrounding catchment using a multi-geophysical approach C. Prinds et al. https://doi.org/10.1016/j.jappgeo.2020.103965
3. Quantifying spatial uncertainty in rock through geostatistical integration of borehole data and a geologist's cross-section D. Boyd et al. https://doi.org/10.1016/j.enggeo.2019.105246
4. Toward real-time three-dimensional mapping of surficial aquifers using a hybrid modeling approach M. Friedel et al. https://doi.org/10.1007/s10040-015-1318-2
5. Assessing hydrological model predictive uncertainty using stochastically generated geological models X. He et al. https://doi.org/10.1002/hyp.10488
6. Markov based transition probability geostatistics in groundwater applications: assumptions and limitations A. Langousis et al. https://doi.org/10.1007/s00477-017-1504-y
7. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models J. Koch et al. https://doi.org/10.5194/gmd-11-1873-2018
8. Enhanced Markov-type Categorical Prediction with geophysical soft constraints for hydrostratigraphic modeling L. Guo et al. https://doi.org/10.5194/hess-30-1421-2026
9. Integrating multiple scales of hydraulic conductivity measurements in training image-based stochastic models K. Mahmud et al. https://doi.org/10.1002/2014WR016150
10. Estimation and scaling of hydrostratigraphic units: application of unsupervised machine learning and multivariate statistical techniques to hydrogeophysical data M. Friedel https://doi.org/10.1007/s10040-016-1452-5
11. Multiple point statistical simulation using uncertain (soft) conditional data T. Hansen et al. https://doi.org/10.1016/j.cageo.2018.01.017
12. 应用随机模拟方法确定复杂裂隙含水层中泉点的捕集区域 Z. Xu et al. https://doi.org/10.3799/dqkx.2023.148
13. Probabilistic predictions using a groundwater model informed with airborne EM data P. Marker et al. https://doi.org/10.1016/j.advwatres.2017.03.002
14. Impact of geostatistical reconstruction approaches on model calibration for flow in highly heterogeneous aquifers M. Siena & M. Riva https://doi.org/10.1007/s00477-020-01865-2
15. Large-Scale $$\hbox {CO}_2$$ CO 2 Storage in a Deep Saline Aquifer: Uncertainties in Predictions Due to Spatial Variability of Flow Parameters and Their Modeling S. Bouquet et al. https://doi.org/10.1007/s11242-015-0590-x
16. Modeling complex aquifer systems: a case study in Baton Rouge, Louisiana (USA) H. Pham & F. Tsai https://doi.org/10.1007/s10040-016-1532-6
17. Modeling the Uncertainty in the Layout of Geological Units by Implicit Boundary Simulation Accounting for a Preexisting Interpretive Geological Model R. Ferrer et al. https://doi.org/10.1007/s11053-021-09964-9
18. Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis X. Cao et al. https://doi.org/10.3390/min14070686
19. A conditional masked autoencoder network based on efficient multiple-head self-attention for characterizing heterogeneous reservoirs Q. Chen et al. https://doi.org/10.1016/j.eswa.2025.128973
20. Integration of Soft Data Into Geostatistical Simulation of Categorical Variables S. Carle & G. Fogg https://doi.org/10.3389/feart.2020.565707
21. Integrated approach for characterizing aquifer heterogeneity in alluvial plains I. Karlović et al. https://doi.org/10.5194/hess-29-4969-2025
22. Computational electromagnetic geophysics for groundwater system studies: A review of established practices and recent advances P. Rulff et al. https://doi.org/10.1016/j.jhydrol.2026.135542
23. Research on appropriate borehole density for establishing reliable geological model based on quantitative uncertainty analysis Q. Sun et al. https://doi.org/10.1007/s12517-019-4533-7
24. Upscaling equivalent hydrofacies simulation based on borehole data generalization and transition probability geostatistics L. Ma et al. https://doi.org/10.1007/s10040-023-02621-w
25. Clustering analysis of a solid waste landfill using ERT and TDIP geophysical data K. Zhai et al. https://doi.org/10.1016/j.wasman.2026.115731
26. MVarGOSIM: an MPS algorithm for characterizing complex structures with multiple variables Y. Chen et al. https://doi.org/10.3389/feart.2025.1756046
27. Finding patterns in subsurface using Bayesian machine learning approach H. Wang https://doi.org/10.1016/j.undsp.2018.10.006
28. Horizon-assisted lithologic modeling: understanding Mississippi Embayment and Coastal Lowlands aquifer systems in Louisiana and southwestern Mississippi, USA S. Yang et al. https://doi.org/10.1007/s10040-024-02804-z
29. Quantifying model structural uncertainty using airborne electromagnetic data B. Minsley et al. https://doi.org/10.1093/gji/ggaa393
30. Hydrofacies simulation based on transition probability geostatistics using electrical resistivity tomography and borehole data L. Ma et al. https://doi.org/10.1007/s10040-022-02539-9
31. Application of time-domain electromagnetic data in 3D mapping of buried paleochannels hosting sandstone type uranium mineralization in Satpura Gonwana basin, India M. Sridhar et al. https://doi.org/10.1016/j.jappgeo.2026.106166
32. Combining 3D geological modelling techniques to address variations in geology, data type and density – An example from Southern Denmark F. Jørgensen et al. https://doi.org/10.1016/j.cageo.2015.04.010
33. Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA W. Trainor-Guitton et al. https://doi.org/10.1144/geoenergy2023-010
34. Estimation of hydraulic conductivity and porosity of a heterogeneous porous aquifer by combining transition probability geostatistical simulation, geophysical survey, and pumping test data V. Amiri et al. https://doi.org/10.1007/s10668-022-02368-6
35. Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics J. Koch et al. https://doi.org/10.1002/2015JD024482
36. A Segmentation Approach for Stochastic Geological Modeling Using Hidden Markov Random Fields H. Wang et al. https://doi.org/10.1007/s11004-016-9663-9
37. Bayesian approach for probabilistic site characterization assimilating borehole experiments and Cone Penetration Tests C. Wang et al. https://doi.org/10.1016/j.enggeo.2016.04.002
38. Three-dimensional geological modelling of AEM resistivity data — A comparison of three methods A. Høyer et al. https://doi.org/10.1016/j.jappgeo.2015.02.005
39. Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics J. Koch et al. https://doi.org/10.1175/JHM-D-16-0148.1
40. Where are the limits of model predictive capabilities? J. Refsgaard et al. https://doi.org/10.1002/hyp.11029
41. Quantifying stratigraphic uncertainty in groundwater modelling for infrastructure design L. Troldborg et al. https://doi.org/10.1007/s10040-021-02303-5