Journal of Visualization

, Volume 17, Issue 3, pp 157–166 | Cite as

Seismic structure extraction based on multi-scale sensitivity analysis

  • Richen Liu
  • Hanqi Guo
  • Xiaoru Yuan
Regular Paper


The exploration of geological composition, e.g. underground flow path, is a significant step for oil and gas search. However, to extract the structural geological composition from the volume, neither the classic volume exploration methods, e.g. transfer function design, nor the traditional volume cut algorithms can be directly used due to its three natural properties, various compositions, discontinuity and noise. In this paper, we present an interactive approach to visualize the structural geological composition with the assistance of multi-scale sensitivity of transfer function. We utilize a slice analyzer to interactively obtain the local transfer function for individual structural geological composition with a carefully designed light-weight transfer function interface guided by the multi-scale sensitivity, which can effectively help the users find the cut-off values of target composition. The final transfer function is shared to 3D volume texture on GPU, and then volume cut methods based on algebraic set operators are utilized to extract the corresponding geological composition in the volume.

Graphical abstract


Seismic visualization Sensitivity analysis Volume rendering 


  1. Amorim R, Brazil EV, Patel D, Sousa MC (2012) Sketch modeling of seismic horizons from uncertainty. In: Proceedings of the International Symposium on Sketch-based interfaces and modelingGoogle Scholar
  2. Aziz IA, Mazelan NA, Samiha N, Mehat M (2008) 3-d seismic visualization using seg-y data format. In: International Symposium on Information TechnologyGoogle Scholar
  3. Brecheisen R, Vilanova A, Platel B (2009) Parameter sensitivity visualization for dti fiber tracking. IEEE Trans Vis Comput Graph 15(6):1441–1448. doi: 10.1109/TVCG.2009.170 CrossRefGoogle Scholar
  4. Chan YH, Correa C, Ma KL (2010) Flow-based scatterplots for sensitivity analysis. In: IEEE Symposium on Visual Analytics Science and Technology, pp 43–50Google Scholar
  5. Chan YH, Correa C, Ma KL (2013) The generalized sensitivity scatterplot. IEEE Trans Vis Comput Graph 19(10)Google Scholar
  6. Guo H, Yuan X (2013) Local wysiwyg volume visualization. In: IEEE Pacific Visualization Symposium, pp 65–72.Google Scholar
  7. Guo H, Mao N, Yuan X (2011) Wysiwyg (what you see is what you get) volume visualization. IEEE Trans Vis Comput Graph 17(12):2106–2114CrossRefGoogle Scholar
  8. Heer J, Kong N, Agrawala M (2009) Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. pp 1303–1312.Google Scholar
  9. Hollt T, Freiler W, Doleisch H, Heinemann G, Hadwiger M (2012) Seivis: an interactive visual subsurface modeling application. IEEE Trans Vis Comput Graph 18(12):2226–2235CrossRefGoogle Scholar
  10. Li D, Sun X, Ren Z, Lin S, Tong Y, Guo B, Zhou K (2013) Transcut: interactive rendering of translucent cutouts. IEEE Trans Vis Comput Graph 19(3):484–494CrossRefGoogle Scholar
  11. Max N, Hanrahan P, Crawfis R (1990) Area and volume coherence for efficient visualization of 3d scalar functions. In: Proceedings of workshop on Volume visualization 24:27–33Google Scholar
  12. Patel D, Giertsen C, Thurmond J, Gjelberg J, Gröller ME (2008) The seismic analyzer: interpreting and illustrating 2d seismic data. IEEE Trans Vis Comput Graph 14:1571–1578CrossRefGoogle Scholar
  13. Ridgley J, Condon S, Hatch J (2013) Geology and oil and gas assessment of the fruitland total petroleum system. U.S. Geological SurveyGoogle Scholar
  14. Yuan X, Zhang N, Nguyen MX, Chen B (2005) Volume cutout. The Visual Computer (Special Issue of Pacific Graphics) 21(8–10):745–754Google Scholar
  15. Zhou L, Hansen C (2014) Guideme: slice-guided semiautomatic multivariate exploration of volumes. In: The Eurographics Conference on Visualization.Google Scholar

Copyright information

© The Visualization Society of Japan 2014

Authors and Affiliations

  1. 1.Key Laboratory of Machine Perception (Ministry of Education), School of EECSPeking UniversityBeijingChina
  2. 2.Center for Computational Science and EngineeringPeking UniversityBeijingChina

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