Articles | Volume 5, issue 2
https://doi.org/10.5194/se-5-1189-2014
© Author(s) 2014. 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-5-1189-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Method article
| 
28 Nov 2014
Method article |
| 28 Nov 2014
Interpretative modelling of a geological cross section from boreholes: sources of uncertainty and their quantification
R. M. Lark
CORRESPONDING AUTHOR
British Geological Survey, Keyworth, Nottingham, UK
S. Thorpe
British Geological Survey, Keyworth, Nottingham, UK
H. Kessler
British Geological Survey, Keyworth, Nottingham, UK
S. J. Mathers
British Geological Survey, Keyworth, Nottingham, UK
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Cited
18 citations as recorded by crossref.
- Uncertainty in geological interpretations: Effectiveness of expert elicitations C. Randle et al. 10.1130/GES01586.1
- Uncertainty in mapped geological boundaries held by a national geological survey:eliciting the geologists' tacit error model R. Lark et al. 10.5194/se-6-727-2015
- Quantifying spatial uncertainty in rock through geostatistical integration of borehole data and a geologist's cross-section D. Boyd et al. 10.1016/j.enggeo.2019.105246
- Drillhole uncertainty propagation for three-dimensional geological modeling using Monte Carlo E. Pakyuz-Charrier et al. 10.1016/j.tecto.2018.09.005
- Hydrogeological Model Selection Among Complex Spatial Priors C. Brunetti et al. 10.1029/2019WR024840
- Decisions, uncertainty and spatial information R. Lark et al. 10.1016/j.spasta.2022.100619
- Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization E. Pakyuz-Charrier et al. 10.5194/se-9-385-2018
- Landscapes and landforms connected with anthropogenic processes over three millennia: The Servian Walls at the Esquiline Hill (Rome, Italy) G. Luberti & M. Del Monte 10.1177/0959683620950460
- 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
- Geotechnical uncertainty, modeling, and decision making K. Phoon et al. 10.1016/j.sandf.2022.101189
- Prior geological knowledge enhanced Markov random field for development of geological cross-sections from sparse data Z. Qian & C. Shi 10.1016/j.compgeo.2024.106587
- Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics S. Garzón et al. 10.1016/j.cageo.2025.106043
- dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs R. Joshi et al. 10.5194/gmd-14-6711-2021
- Quantification of the uncertainty of geoscientific maps relying on human sensory engagement J. Asadi et al. 10.1007/s12665-024-11870-1
- An automated method for topology consistent processing of parallel geological cross-sections based on topology reasoning Y. Chen et al. 10.1016/j.cageo.2023.105442
- Can uncertainty in geological cross-section interpretations be quantified and predicted? C. Randle et al. 10.1130/GES01510.1
- Comparing geomorphological maps made manually and by deep learning W. van der Meij et al. 10.1002/esp.5305
- Fracking bad language – hydraulic fracturing and earthquake risks J. Roberts et al. 10.5194/gc-4-303-2021
18 citations as recorded by crossref.
- Uncertainty in geological interpretations: Effectiveness of expert elicitations C. Randle et al. 10.1130/GES01586.1
- Uncertainty in mapped geological boundaries held by a national geological survey:eliciting the geologists' tacit error model R. Lark et al. 10.5194/se-6-727-2015
- Quantifying spatial uncertainty in rock through geostatistical integration of borehole data and a geologist's cross-section D. Boyd et al. 10.1016/j.enggeo.2019.105246
- Drillhole uncertainty propagation for three-dimensional geological modeling using Monte Carlo E. Pakyuz-Charrier et al. 10.1016/j.tecto.2018.09.005
- Hydrogeological Model Selection Among Complex Spatial Priors C. Brunetti et al. 10.1029/2019WR024840
- Decisions, uncertainty and spatial information R. Lark et al. 10.1016/j.spasta.2022.100619
- Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization E. Pakyuz-Charrier et al. 10.5194/se-9-385-2018
- Landscapes and landforms connected with anthropogenic processes over three millennia: The Servian Walls at the Esquiline Hill (Rome, Italy) G. Luberti & M. Del Monte 10.1177/0959683620950460
- 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
- Geotechnical uncertainty, modeling, and decision making K. Phoon et al. 10.1016/j.sandf.2022.101189
- Prior geological knowledge enhanced Markov random field for development of geological cross-sections from sparse data Z. Qian & C. Shi 10.1016/j.compgeo.2024.106587
- Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics S. Garzón et al. 10.1016/j.cageo.2025.106043
- dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs R. Joshi et al. 10.5194/gmd-14-6711-2021
- Quantification of the uncertainty of geoscientific maps relying on human sensory engagement J. Asadi et al. 10.1007/s12665-024-11870-1
- An automated method for topology consistent processing of parallel geological cross-sections based on topology reasoning Y. Chen et al. 10.1016/j.cageo.2023.105442
- Can uncertainty in geological cross-section interpretations be quantified and predicted? C. Randle et al. 10.1130/GES01510.1
- Comparing geomorphological maps made manually and by deep learning W. van der Meij et al. 10.1002/esp.5305
- Fracking bad language – hydraulic fracturing and earthquake risks J. Roberts et al. 10.5194/gc-4-303-2021
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Latest update: 09 Oct 2025
Short summary
Geological information for users such as planners, miners or engineers depends on limited observations and the interpretative skills of the geologist. It therefore has an attendant uncertainty which must be quantified so that the data user can account for it. In this paper, we describe an experiment to identify and quantify the sources of uncertainty in geologists' interpretations of boreholes along a cross section.
Geological information for users such as planners, miners or engineers depends on limited...
