Skip to main content
Log in

Uncertainty analysis and visualization of geological subsurface and its application in metro station construction

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

To visualize and analyze the impact of uncertainty on the geological subsurface, on the term of the geological attribute probabilities (GAP), a vector parameters-based method is presented. Perturbing local data with error distribution, a GAP isosurface suite is first obtained by the Monte Carlo simulation. Several vector parameters including normal vector, curvatures and their entropy are used to measure uncertainties of the isosurface suite. The vector parameters except curvature and curvature entropy are visualized as line features by distributing them over their respective equivalent structure surfaces or concentrating on the initial surface. The curvature and curvature entropy presented with color map to reveal the geometrical variation on the perturbed zone. The multiple-dimensional scaling (MDS) method is used to map GAP isosurfaces to a set of points in low-dimensional space to obtain the total diversity among these equivalent probability surfaces. An example of a bedrock surface structure in a metro station shows that the presented method is applicable to quantitative description and visualization of uncertainties in geological subsurface. MDS plots shows differences of total diversity caused by different error distribution parameters or different distribution types.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bárdossy G, Fodor J (2004). Evaluation of Uncertainties and Risks in Geology-New Mathematical Approaches for Their Handling. New York: Springer Press

    Book  Google Scholar 

  • Bistacchi A, Massironi M, Dal Piaz G V, Dal Piaz G, Monopoli B, Schiavo A, Toffolon G (2008). 3D fold and fault reconstruction with an uncertainty model: an example from an Alpine tunnel case study. Comput Geosci, 34(4): 351–372

    Article  Google Scholar 

  • Bond C E (2015). Uncertainty in structural interpretation: lessons to be learnt. J Struct Geol, 74: 185–200

    Article  Google Scholar 

  • Bond C E, Gibbs A D, Shipton Z K, Jones S (2007). What do you think this is? “Conceptual uncertainty” in geoscience interpretation. GSA Today, 17(11): 4–10

    Article  Google Scholar 

  • Borg I, Groenen P (1997). Modern Multidimensional Scaling: Theory and Applications. New York: Springer

    Book  Google Scholar 

  • Caers J (2011). Modelling Uncertainty in the Earth Sciences. Chichester: Wiley-Blackwell

    Book  Google Scholar 

  • Calcagno P, Chilès J P, Courrioux G, Guillen A (2008). Geological modelling from field data and geological knowledge: part I. modelling method coupling 3D potential field interpolation and geological rules. Phys Earth Planet Inter, 171(1–4): 147–157

    Article  Google Scholar 

  • Caumon G, Collon-Drouaillet P, Le Carlier de Veslud C, Viseur S, Sausse J (2009). Surface-based 3D modeling of geological structures. Math Geosci, 41(8): 927–945

    Article  Google Scholar 

  • Chilès J P, Aug C, Guillen A, Lees T (2004) Modelling of geometry of geological units and its uncertainty in 3D from structural data: the potential-field method. In: Ore body Modelling and Strategic Mine Planning-Uncertainty and Risk Management Models

  • de Kemp E A, Schetselaar M E, Hillier J M, Lydon W J, Ransom W P (2016). Assessing the workflow for regional-scale 3D geologic modeling: an example from the Sullivan time horizon, Purcell Anticlinorium East Kootenay region, southeastern British Columbia. Interpretation (Tulsa), 4(3): SM33–SM50

    Article  Google Scholar 

  • Dong C S, Wang G Z (2005). Curvature estimation on triangular mesh. J Zhejiang Univ Sci A, 6: 128–136

    Article  Google Scholar 

  • González-Garcia J, Jessell M (2016). A 3D geological model for the Ruiz-Tolima Volcanic Massif (Colombia): assessment of geological uncertainty using a stochastic approach based on Bézier curve design. Tectonophysics, 687: 139–157

    Article  Google Scholar 

  • Gregoire M, Caers J (2015). Multiple-Point Geostatistics: Stochastic Modeling with Training Images. New York: John Wiley & Sons

    Google Scholar 

  • Guangdong Geological and Mineral Bureau (1989). 1:50,000 Regional geological survey report of Guangzhou area

  • Guillen A, Calcagno P, Courrioux G, Joly A, Ledru P (2008). Geological modeling from field data and geological knowledge: part II modelling validation using gravity and magnetic data inversion. Phys Earth Planet Inter, 171: 158–169

    Article  Google Scholar 

  • Hollister B E (2015). Visualizing multimodal uncertainty in ensemble vector fields. Dissertation for Doctor Degree. Santa Cruz: UC Santa Cruz

    Google Scholar 

  • Hou W S, Cui C J, Yang L, Yang Q C, Clarke K (2019). Entropy-based weighting in one-dimensional multiple errors analysis of geological contacts to model geological structure. Math Geosci, 51(1): 29–51

    Article  Google Scholar 

  • Jessell W M, Ailleres L, de Kemp A E (2010). Towards an integrated inversion of geoscientific data: What price of geology? Tectonophys, 490(3–4): 294–306

    Article  Google Scholar 

  • Jones R R, McCaffrey J K, Wilson W R, Holdsworth E R (2004). Digital field data acquisition: towards increased quantification of uncertainty during geological mapping. Geol Soc Lond Spec Publ, 239(1): 43–56

    Article  Google Scholar 

  • Julio C, Caumon G, Ford M (2015). Sampling the uncertainty associated with segmented normal fault interpretation using a stochastic downscaling method. Tectonophysics, 639: 56–67

    Article  Google Scholar 

  • Lee K, Jung S, Choe J (2016). Ensemble smoother with clustered covariance for 3D channelized reservoirs with geological uncertainty. J Petrol Sci Eng, 145: 423–435

    Article  Google Scholar 

  • Li X, Li P, Zhu H (2013). Coal seam surface modeling and updating with multi-source data integration using Bayesian Geostatistics. Eng Geol, 164: 208–221

    Article  Google Scholar 

  • Li X Y, Zhang F, Zhu H J, Hu W, Li W (2015). A digital elevation model (DEM) clustering simplification algorithm based on curvature entropy and the Guassian mixture model. J Beijing Chem Tech (Natural Science Edition), 42(6): 103–108 (in Chinese)

    Google Scholar 

  • Lindsay M D, Aillères L, Jessell M W, de Kemp E A, Betts P G (2012). Locating and quantifying geological uncertainty in three-dimensional models: analysis of the Gippsland Basin, southeastern Australia. Tectonophys, 546–547(3): 10–27

    Article  Google Scholar 

  • Lindsay M D, Jessell M W, Ailleres L, Perrouty S, de Kemp E, Betts P G (2013). Geodiversity: exploration of 3D geological model space. Tectonophys, 594: 27–37

    Article  Google Scholar 

  • Mann C J (1993). Uncertainty in geology. In: Davis C J, Herzfeld U C, eds. Computers in Geology-25 Years of Progress. New York: Oxford University Press, 241–254

    Google Scholar 

  • Scheidt C, Jeong C, Mukerji T, Caers J (2015). Probabilistic falsification of prior geologic uncertainty with seismic amplitude data: application to a turbidite reservoir case. Geophysics, 80(5): M89–M12

    Article  Google Scholar 

  • Tacher L, Pomian-Srzednicki I, Parriaux A (2006). Geological uncertainties associated with 3-D subsurface models. Comput Geosci, 32(2): 212–221

    Article  Google Scholar 

  • Thore P, Shtuka A, Lecour M, Ait-Ettajer T, Cognot R (2002). Structural uncertainties: determination, management and applications. Geophysics, 67(3): 840–852

    Article  Google Scholar 

  • Turner A K, Kessler H (2015). Challenges with applying geological modelling for infrastructure design. In: Schaeben H, Tolosana Delgado R, van den Boogaart K G, van den Boogaart R, eds. Proceedings of IAMG 2015, Freiberg (Saxony), Germany, 2015, 49–58

  • Wellmann J F, Horowitz F G, Schill E, Regenauer-Lieb K (2010). Towards incorporating uncertainty of structural data in 3D geological inversion. Tectonophys, 490(3–4): 141–151

    Article  Google Scholar 

  • Wellmann F J, Regenauer-Lieb K (2012). Uncertainties have a meaning: information entropy as a quality measure for 3-D geological models. Tectonophys, 526–529: 207–216

    Article  Google Scholar 

Download references

Acknowledgements

This research was substantially supported by the National Natural Science Foundation of China Program (Grant Nos. 41472300 and 41772345), and Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311021003).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Weisheng Hou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, W., Yang, Q., Chen, X. et al. Uncertainty analysis and visualization of geological subsurface and its application in metro station construction. Front. Earth Sci. 15, 692–704 (2021). https://doi.org/10.1007/s11707-021-0897-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-021-0897-6

Keywords

Navigation