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您的检索式：关键字=Generative Adversarial Networks
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| 1 | Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks显示文摘This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data.Compared with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural network.The networks are trained in an adversarial manner until the generator can create 'fake' images that the discriminator cannot distinguish from 'real' images.We extend the original GAN approach to 3D geological modeling at the reservoir scale.The GANs are trained using a library of 3D facies models.Once the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data interpretations.This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends.The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend. | Tuan-Feng Zhang Peter Tilke Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey | 2019 | Petroleum Science2019，16，3： | 16 |
| 2 | ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks显示文摘We introduce ShadowGAN, a generative adversarial network(GAN) for synthesizing shadows for virtual objects inserted in images. Given a target image containing several existing objects with shadows,and an input source object with a specified insertion position, the network generates a realistic shadow for the source object. The shadow is synthesized by a generator; using the proposed local adversarial and global adversarial discriminators, the synthetic shadow's appearance is locally realistic in shape, and globally consistent with other objects' shadows in terms of shadow direction and area. To overcome the lack of training data,we produced training samples based on public 3D models and rendering technology. Experimental results from a user study show that the synthetic shadowed results look natural and authentic. | Shuyang Zhang Runze Liang Miao Wang | 2019 | Computational Visual Media2019，5，1： | 5 |
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