Articles | Volume 6, issue 2
https://doi.org/10.5194/se-6-727-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/se-6-727-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Method article
| 
17 Jun 2015
Method article |
| 17 Jun 2015
Uncertainty in mapped geological boundaries held by a national geological survey:eliciting the geologists' tacit error model
R. M. Lark
CORRESPONDING AUTHOR
British Geological Survey, Keyworth, Nottingham, UK
R. S. Lawley
British Geological Survey, Keyworth, Nottingham, UK
A. J. M. Barron
British Geological Survey, Keyworth, Nottingham, UK
D. T. Aldiss
British Geological Survey, Keyworth, Nottingham, UK
K. Ambrose
British Geological Survey, Keyworth, Nottingham, UK
A. H. Cooper
British Geological Survey, Keyworth, Nottingham, UK
J. R. Lee
British Geological Survey, Keyworth, Nottingham, UK
C. N. Waters
British Geological Survey, Keyworth, Nottingham, UK
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Cited
14 citations as recorded by crossref.
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- Unsupervised classification applications in enhancing lithological mapping and geological understanding: a case study from Northern Ireland Z. Smillie et al. 10.1144/jgs2022-136
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- Imaging the Subsurface: How Different Visualizations of Cross-sections Affect the Sense of Uncertainty A. Bang-Kittilsen & T. Midtbø 10.1007/s41651-020-00071-6
- Quantification of the uncertainty of geoscientific maps relying on human sensory engagement J. Asadi et al. 10.1007/s12665-024-11870-1
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- Machine learning for surficial geologic mapping S. Johnson & W. Haneberg 10.1002/esp.6032
- Redefining the boundary between crystalline and sedimentary rock of Eastern Dahomey Basin G. Mosuro et al. 10.1038/s41598-021-84687-8
- Integrating deterministic lithostratigraphic models in stochastic realizations of subsurface heterogeneity. Impact on predictions of lithology, hydraulic heads and groundwater fluxes M. Bianchi et al. 10.1016/j.jhydrol.2015.10.072
- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. 10.5194/essd-14-381-2022
- Information Data in Geological Informatization Based on Cloud Computing Y. Li 10.1088/1757-899X/750/1/012158
13 citations as recorded by crossref.
- Uncertainty in geological interpretations: Effectiveness of expert elicitations C. Randle et al. 10.1130/GES01586.1
- Method selection in short-term eruption forecasting M. Whitehead & M. Bebbington 10.1016/j.jvolgeores.2021.107386
- Expert elicitation of directional metocean parameters L. Astfalck et al. 10.1016/j.oceaneng.2018.04.047
- Unsupervised classification applications in enhancing lithological mapping and geological understanding: a case study from Northern Ireland Z. Smillie et al. 10.1144/jgs2022-136
- MM3: Multimodal framework for regional-scale quantitative landslide risk analysis W. Pollock & J. Wartman 10.1016/j.mex.2025.103218
- Compiling regional structures in geological databases: The giant quartz veins of the Pyrenees as a case study E. González-Esvertit et al. 10.1016/j.jsg.2022.104705
- Imaging the Subsurface: How Different Visualizations of Cross-sections Affect the Sense of Uncertainty A. Bang-Kittilsen & T. Midtbø 10.1007/s41651-020-00071-6
- Quantification of the uncertainty of geoscientific maps relying on human sensory engagement J. Asadi et al. 10.1007/s12665-024-11870-1
- 3D Parametric Modeling of Complex Geological Structures for Geotechnical Engineering of Dam Foundation Based on T‐Splines Y. Zhang et al. 10.1111/mice.12343
- Machine learning for surficial geologic mapping S. Johnson & W. Haneberg 10.1002/esp.6032
- Redefining the boundary between crystalline and sedimentary rock of Eastern Dahomey Basin G. Mosuro et al. 10.1038/s41598-021-84687-8
- Integrating deterministic lithostratigraphic models in stochastic realizations of subsurface heterogeneity. Impact on predictions of lithology, hydraulic heads and groundwater fluxes M. Bianchi et al. 10.1016/j.jhydrol.2015.10.072
- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. 10.5194/essd-14-381-2022
1 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 05 Jul 2025
Short summary
Boundaries on geological maps are drawn by geologists interpreting evidence (boreholes, surface features, etc.) in light of their geological knowledge. Boundaries are therefore uncertain, and this must be quantified so that map users can make sound decisions. In this paper we show how we can find statistical models of the uncertainty of boundary positions by a structured method to elicit the tacit model of uncertainty which experienced field geologists use when interpreting geological maps.
Boundaries on geological maps are drawn by geologists interpreting evidence (boreholes, surface...
