Several works have investigated the use of sketches to facilitate the creation of models by providing a faster and more intuitive tool set. Approaches that are already consolidated in domains such as architecture have proved to be much more difficult when applied to 3D geological modelling, given the size of the solution space. Nevertheless, more specific sketch-based applications have been proposed in geosciences such as seismic horizon modelling, geological storytelling, 3D fluvial system modelling and a tentative generic 3D modelling tool for geology. In this work, we investigate the applicability of deep generative networks for a simpler task: synthesizing seismic sections. Following the rising interest of the geoscience community in generative models such as Generative Adversarial Networks (GANs), we train a conditional GAN to generate seismic sections from simple sketches. The results are promising and find application in many tasks such as sketch-based seismic image retrieval and the generation of training data for machine learning algorithms such as Convolutional Neural Networks (CNNs).


Article metrics loading...

Loading full text...

Full text loading...


  1. Amorim, R., Brazil, E. V., Patel, D., and Sousa, M. C.
    [2012] Sketch modeling of seismic horizons from uncertainty. In Proceedings of the International Symposium on Sketch-based interfaces and modeling (pp. 1–10). Eurographics Association.
    [Google Scholar]
  2. Canny, J.
    [1986] A Computational Approach to Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679–698.
    [Google Scholar]
  3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., OzairS. and Bengio, Y.
    [2014] Generative adversarial nets. In Advances in NIPS (pp. 2672–2680).
    [Google Scholar]
  4. Hu, X., Cai, L., and Wang, Q.
    [2016] Interactive and stochastic complex geological-surface reconstruction based on sketch. In SEG Technical Program Expanded Abstracts 2016 (pp. 1997–2002). Society of Exploration Geophysicists.
    [Google Scholar]
  5. Isola, P., Zhu, J. Y., Zhou, T. and Efros, A. A.
    [2017] Image-to-image translation with conditional adversarial networks. arXiv preprint.
    [Google Scholar]
  6. Lidal, E. M., Hauser, H., and Viola, I.
    [2012] Geological storytelling: graphically exploring and communicating geological sketches. In Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling (pp. 11–20). Eurographics Association.
    [Google Scholar]
  7. Lidal, E. M., Patel, D., Bendiksen, M., Langeland, T., and Viola, I.
    [2013] Rapid sketch-based 3d modeling of geology. In Workshop on Visualisation in Environmental Science (pp. 1–5).
    [Google Scholar]
  8. Mosser, L., Kimman, W., Dramsch, J., Purves, S., De la Fuente Briceño, A., and Ganssle, G.
    [2018] Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks. In 80th EAGE Conference and Exhibition 2018.
    [Google Scholar]
  9. Oliveira, D. A., Ferreira, R. S., Silva, R., and Brazil, E. V.
    [2018] Interpolating Seismic Data With Conditional Generative Adversarial Networks. IEEE Geoscience and Remote Sensing Letters, (99)1–5.
    [Google Scholar]
  10. Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., and Yu, T.
    [2014] scikit-image: image processing in Python. PeerJ, 2, e453.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error