176
ACKNOWLEDGMENTS
The authors acknowledge the funding provided by the Natural Sciences and Engineering
Council of Canada (NSERC), funding reference number RGPIN-2017-04200 and RGPAS2017-507956, and the Computer and Geoscience Research Scholarship 2018.
REFERENCES
Arpat, G.B. & Caers, J. Conditional simulation with patterns. Mathematical Geology, 39 (2):177–203,
2007.
Caers, J. & Journel, A.G. Stochastic reservoir simulation using neural networks trained on outcrop data. In
SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1998.
Daly, C. Higher Order Models using Entropy, Markov Random Fields and Sequential Simulation. In
O. Leuangthong and C. Deutsch, editors, Geostatistics Banff 2004 (Quantitative Geology and
Geostatistics, vol. 14), pages 215–224, 2004.
Dosovitskiy, A., Springenberg, J., & Brox, T. Learning to generate chairs with convolutional neural
networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
pages 1538–1546, 2015.
Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R., & Unger, J. HDR image reconstruction from a
single exposure using deep CNNs. ACM Transactions on Graphics (TOG), 36(6):178, 2017.
Gatys, L., Ecker, A., & Bethge, M. Image style transfer using convolutional neural networks. In Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2414–2423, 2016.
Goodfellow, I., Bengio, Y., & Courville, A. Deep learning. Vol. 1. MIT press Cambridge, 2016.
He, K., Zhang, X., Ren, S., & Sun, K. Deep residual learning for image recognition. In Proceedings of the
IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
Hornik, K., Stinchcombe, M, & White, H. Multilayer feedforward networks are universal approximators.
Neural networks, 2(5):359–366, 1989.
Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
Kingma, D. & Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980,
2014.
Laloy, E., Herault, R., Jacques, D., & Linde, N. Training Image Based Geostatistical Inversion Using a
Spatial Generative Adversarial Neural Network. Water Resources Research, 54(1):381–406, 2018.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. Gradient-based learning applied to document recognition.
Proceedings of the IEEE, 86(11): 2278–2324, 1998.
Mariethoz, G. & Caers, J. Multiple-point geostatistics: stochastic modeling with training images.
John Wiley & Sons, 2014.
Mariethoz, G., Renard, P. The direct sampling method to perform multiple point geostatistical simulations.
Water Resources Research, 46(11), 2010. W11536.
Parra, A. & Ortiz, J. Adapting a texture synthesis algorithm for conditional multiple point geostatistical
simulation. Stochastic Environmental Research and Risk Assessment, 25(8):1101–1111, 2011.
Strebelle, S. Conditional simulation of complex geological structures using multiple point statistics.
Mathematical geology, 34(1):1–21, 2002.
Tahmasebi, P. Multiple Point Statistics: A Review. Handbook of Mathematical Geosciences, page 613, 2018.
Zahner, T., Lochbuhler, T., Mariethoz, G., & Linde, N. Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion. Geophysical Journal International, 204(2):1179–1190, 2015.
ACKNOWLEDGMENTS
The authors acknowledge the funding provided by the Natural Sciences and Engineering
Council of Canada (NSERC), funding reference number RGPIN-2017-04200 and RGPAS2017-507956, and the Computer and Geoscience Research Scholarship 2018.
REFERENCES
Arpat, G.B. & Caers, J. Conditional simulation with patterns. Mathematical Geology, 39 (2):177–203,
2007.
Caers, J. & Journel, A.G. Stochastic reservoir simulation using neural networks trained on outcrop data. In
SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1998.
Daly, C. Higher Order Models using Entropy, Markov Random Fields and Sequential Simulation. In
O. Leuangthong and C. Deutsch, editors, Geostatistics Banff 2004 (Quantitative Geology and
Geostatistics, vol. 14), pages 215–224, 2004.
Dosovitskiy, A., Springenberg, J., & Brox, T. Learning to generate chairs with convolutional neural
networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
pages 1538–1546, 2015.
Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R., & Unger, J. HDR image reconstruction from a
single exposure using deep CNNs. ACM Transactions on Graphics (TOG), 36(6):178, 2017.
Gatys, L., Ecker, A., & Bethge, M. Image style transfer using convolutional neural networks. In Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2414–2423, 2016.
Goodfellow, I., Bengio, Y., & Courville, A. Deep learning. Vol. 1. MIT press Cambridge, 2016.
He, K., Zhang, X., Ren, S., & Sun, K. Deep residual learning for image recognition. In Proceedings of the
IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
Hornik, K., Stinchcombe, M, & White, H. Multilayer feedforward networks are universal approximators.
Neural networks, 2(5):359–366, 1989.
Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
Kingma, D. & Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980,
2014.
Laloy, E., Herault, R., Jacques, D., & Linde, N. Training Image Based Geostatistical Inversion Using a
Spatial Generative Adversarial Neural Network. Water Resources Research, 54(1):381–406, 2018.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. Gradient-based learning applied to document recognition.
Proceedings of the IEEE, 86(11): 2278–2324, 1998.
Mariethoz, G. & Caers, J. Multiple-point geostatistics: stochastic modeling with training images.
John Wiley & Sons, 2014.
Mariethoz, G., Renard, P. The direct sampling method to perform multiple point geostatistical simulations.
Water Resources Research, 46(11), 2010. W11536.
Parra, A. & Ortiz, J. Adapting a texture synthesis algorithm for conditional multiple point geostatistical
simulation. Stochastic Environmental Research and Risk Assessment, 25(8):1101–1111, 2011.
Strebelle, S. Conditional simulation of complex geological structures using multiple point statistics.
Mathematical geology, 34(1):1–21, 2002.
Tahmasebi, P. Multiple Point Statistics: A Review. Handbook of Mathematical Geosciences, page 613, 2018.
Zahner, T., Lochbuhler, T., Mariethoz, G., & Linde, N. Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion. Geophysical Journal International, 204(2):1179–1190, 2015.