79 citations as recorded by crossref.

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22. Data-Driven Refueling Strategies and Infrastructure Design for H2 Cargo Bike Fleets via H2 Tank Swapping S. Skarlis et al. https://doi.org/10.3390/futuretransp6040163
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25. 3D probabilistic geology differentiation based on airborne geophysics, mixed Lp norm joint inversion, and physical property measurements X. Wei & J. Sun https://doi.org/10.1190/geo2021-0833.1
26. Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success R. Scalzo et al. https://doi.org/10.5194/gmd-12-2941-2019
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28. Topological analysis in Monte Carlo simulation for uncertainty propagation E. Pakyuz-Charrier et al. https://doi.org/10.5194/se-10-1663-2019
29. Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method M. Abbaszadeh et al. https://doi.org/10.1016/j.cageo.2022.105140
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33. Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application J. Giraud et al. https://doi.org/10.5194/se-14-43-2023
34. Geological domain delineation, uncertainty quantification, and cut-off-based contained gold screening using unsupervised learning in a Peruvian gold deposit M. Cotrina-Teatino & J. Araujo https://doi.org/10.1007/s12145-026-02128-4
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36. 3D structural and probabilistic modelling of geothermal reservoir horizons in the Northern Eifel and its foreland A. Jüstel et al. https://doi.org/10.1127/zdgg/2025/0436
37. Assessing geometrical uncertainties in geological interface models using Markov chain Monte Carlo sampling via abstract graph J. Huang et al. https://doi.org/10.1016/j.tecto.2023.230032
38. Triangulated irregular network-based probabilistic 3D geological modelling using Markov Chain and Monte Carlo simulation J. Ouyang et al. https://doi.org/10.1016/j.enggeo.2023.107131
39. Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS) M. Yousefi et al. https://doi.org/10.1016/j.oregeorev.2024.105930
40. Spatial uncertainty constraints reduce overfitting for potential field geophysical inversion M. Lindsay et al. https://doi.org/10.1093/gji/ggag133
41. Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure K. Sharma et al. https://doi.org/10.1109/ACCESS.2021.3083969
42. A general frame for uncertainty propagation under multimodally distributed random variables X. Meng et al. https://doi.org/10.1016/j.cma.2020.113109
43. Uncertainty assessment of a 3D geological model by integrating data errors, spatial variations and cognition bias D. Liang et al. https://doi.org/10.1007/s12145-020-00548-4
44. Category-based fractal modelling: A novel model to integrate the geology into the data for more effective processing and interpretation B. Sadeghi & D. Cohen https://doi.org/10.1016/j.gexplo.2021.106783
45. Pseudo trans-dimensional 3-D geometrical inversion: a proof of concept using gravity data J. Giraud et al. https://doi.org/10.1093/gji/ggaf501
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48. Modeling uncertain data using Monte Carlo integration method for clustering K. Sharma & A. Seal https://doi.org/10.1016/j.eswa.2019.06.050
49. Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging J. Giraud et al. https://doi.org/10.5194/se-11-419-2020
50. Three-dimensional bedrock implicit modeling and uncertainty quantification from sparse geological map data W. Lv & X. Liu https://doi.org/10.1016/j.jsg.2026.105627
51. Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models H. Olierook et al. https://doi.org/10.1016/j.gsf.2020.04.015
52. Explicit-implicit-integrated 3-D geological modelling approach: A case study of the Xianyan Demolition Volcano (Fujian, China) J. Guo et al. https://doi.org/10.1016/j.tecto.2020.228648
53. Geological uncertainty assessment for rock tunnelling using spatial stochastic modeling Y. Li et al. https://doi.org/10.1007/s12145-026-02166-y
54. The role of geological models and uncertainties in safety assessments M. Bjorge et al. https://doi.org/10.1007/s12665-022-10305-z
55. Multi-view spectral clustering for uncertain objects K. Sharma & A. Seal https://doi.org/10.1016/j.ins.2020.08.080
56. Regional groundwater flow and karst evolution–theoretical approach and example from Switzerland S. Scheidler et al. https://doi.org/10.1007/s12665-021-09471-3
57. Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data V. Ogarko et al. https://doi.org/10.1190/geo2019-0633.1
58. A simulation-based framework for modulating the effects of subjectivity in greenfield Mineral Prospectivity Mapping with geochemical and geological data M. Parsa & A. Pour https://doi.org/10.1016/j.gexplo.2021.106838
59. Estimating relative uncertainty of geological 3D models with low density of input data in geologically complex regions F. Staněk et al. https://doi.org/10.1007/s12145-025-01778-0
60. Interpretation and representation in geomodels: the POKIMON ontology for formalizing geomodelling knowledge I. Laouici et al. https://doi.org/10.5194/gmd-19-6517-2026
61. Knowledge-driven stochastic modeling of geological geometry features conditioned on drillholes and outcrop contacts X. Wei et al. https://doi.org/10.1016/j.cageo.2024.105779
62. Concentration-distance from centroids (C-DC) multifractal modeling: A novel approach to characterizing geochemical patterns based on sample distance from mineralization B. Sadeghi & D. Cohen https://doi.org/10.1016/j.oregeorev.2021.104302
63. Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis J. Giraud et al. https://doi.org/10.1093/gji/ggz152
64. loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification G. Pirot et al. https://doi.org/10.5194/gmd-15-4689-2022
65. 3D geological structure inversion from Noddy-generated magnetic data using deep learning methods J. Guo et al. https://doi.org/10.1016/j.cageo.2021.104701
66. Twin support vector regression for characterizing uncertainty in surface reconstruction S. Yu et al. https://doi.org/10.1038/s41598-024-70109-y
67. Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis L. Wang et al. https://doi.org/10.1016/j.cageo.2023.105419
68. Representing the Uncertainty of a 3D Geological Model via Global Optimum Truth Discovery Technology N. Li et al. https://doi.org/10.1007/s11053-024-10404-7
69. Relationship between well pattern density and variation function of stochastic modelling and database establishment J. Wang et al. https://doi.org/10.2516/ogst/2020077
70. Multi‐scenario Interpretations From Sparse Fault Evidence Using Graph Theory and Geological Rules G. Godefroy et al. https://doi.org/10.1029/2020JB020022
71. 3D structural implicit modelling of folded metamorphic units at Lago di Cignana with uncertainty assessment G. Arienti et al. https://doi.org/10.1016/j.jsg.2024.105329
72. Dynamic uncertainty propagation analysis framework for nonlinear control problem based on manifold learning and optimal polynomial method and its application on active suspension system J. Liu et al. https://doi.org/10.1177/10775463241276242
73. Reproducibility of atomistic friction computer experiments: a molecular dynamics simulation study S. Stephan et al. https://doi.org/10.1080/08927022.2021.1987430
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75. Three-Dimensional Geological Modeling Method Based on Potential Vector Fields P. Liu et al. https://doi.org/10.3390/app15073594
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78. 3D geological modeling of deep fractured low porosity sandstone gas reservoir in the Kuqa Depression, Tarim Basin Z. Liu et al. https://doi.org/10.3389/feart.2023.1171050
79. Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning A. Schaaf & C. Bond https://doi.org/10.5194/se-10-1049-2019