中文 | English

J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

3D digital core reconstruction based on gradient penalized Wasserstein generative adversarial networks

Download Full Text

基于梯度惩罚 Wasserstein 生成对抗网络的数字岩心重建

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Nov.2024, Vol.47 No.11: 1559-1563

DOI: 10.3969/j.issn.1003-5060.2024.11.019

Authors

XU Huibing, LI Daolun, ZHA Wenshu

(School of Mathematics, Hefei University of Technology, Hefei 230601, China)

Abstract and Keywords

Abstract: In view of the problem of gradient instability in the reconstruction of 3D digital cores based on Wasserstein generative adversarial networks (WGAN), this paper proposes a Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) algorithm. A convolutional network is used to build a generative network to learn the distribution of the real samples, and then a discriminative network is built to distinguish the reconstructed samples from the real samples. As the weight cropping of WGAN leads to uneven weight dispersion, WGAN-GP adds a gradient penalty term to make the gradient distribution more uniform and speed up the convergence of the network, allowing for more stable training. A comparison of porosity, specific surface area and Euler characteristics shows that the WGAN-GP reconstruction algorithm is more effective in reproducing the 3D pore structure characteristics of rocks than the WGAN reconstruction algorithm.

Keywords: digital cores; generative adversarial networks(GAN); gradient penalty; 3D reconstruction; convolutional neural networks

作者信息

徐慧兵，李道伦，查文舒

（合肥工业大学数学学院，安徽合肥230601）

摘要和关键词

摘要: 文章针对三维 Wasserstein 生成对抗网络（Wasserstein generative adversarial networks，WGAN）重建数字岩心的梯度不稳定问题，提出一种基于带梯度惩罚的 Wasserstein 生成对抗网络（Wasserstein generative adversarial networks with gradient penalty，WGAN-GP）三维数字岩心重建算法。首先利用卷积神经网络构建生成网络学习真实样本的分布，然后再构建判别网络以区分重建样本和真实样本。由于 WGAN 的权值裁剪导致权重分散不均匀，WGAN-GP 增加了梯度惩罚项，使得梯度分布更加均匀并加快网络收敛速度，让训练更加稳定。实验通过孔隙度、比表面积和欧拉特性的对比表明，相比于 WGAN 算法，WGAN-GP 三维数字岩心重建算法能更加有效地重现岩石的三维孔隙结构特征。

关键词: 数字岩心；生成对抗网络（GAN）；梯度惩罚；三维重建；卷积神经网络

Funding

国家自然科学基金资助项目（12172115）

Previous Next

Back to Contents