Abstract: Aiming at the low efficiency and the complex simulation process of the traditional reconstruction methods for porous media such as Multi-Point Statistics(MPS)which require scanning the training image many times and to obtain simulation results by complex probability calculations,a method to reconstruct porous media using adaptive deep transfer learning was presented. Firstly,deep neural network was used to extract the complex features from the training image of porous media. Secondly,the adaptive layer was added in deep transfer learning to reduce the difference in data distribution between training data and prediction data. Finally,through copying features by transfer learning,the simulation result consistent with the real training data was obtained. The performance of the proposed method was evaluated by comparing with the classical porous media reconstruction method MPS in multiple-point connectivity curve,variogram curve and porosity. The results indicate that the proposed method has high reconstruction quality. Meanwhile,the method has the average running time reduced from 840 s to 166 s,the average CPU usage dropped from 98% to 20%,and the average memory utilization decreased by 69%. The proposed method significantly improves the efficiency of porous media reconstruction under the premise of ensuring better quality of reconstruction results.

Key words: porous media, deep learning, adaptive transfer learning, training image

摘要： 目前用于多孔介质重构的多点统计法（MPS）等传统方法需要多次扫描训练图像，然后进行后续复杂的概率计算得到模拟结果，导致重构效率较低，模拟过程复杂，因此提出一种基于自适应深度迁移学习的重构方法。首先利用深度神经网络从多孔介质的训练图像中提取复杂特征，然后在深度迁移学习中添加自适应层以减少训练数据和预测数据之间的数据分布差异，最后使用自适应迁移学习复制这些特征来获得与真实训练数据结构相似的重构结果。通过与典型的多孔介质重构方法MPS的比较实验，结果显示在多点连通曲线、变差函数曲线和孔隙度方面，该方法重构质量更好，平均重构耗时从840 s减少到166 s，平均CPU占用率从98%下降到20%，平均内存占用下降了69%。所提方法在保证重构结果质量更好的前提下，显著提高了多孔介质重构的效率。

关键词: 多孔介质, 深度学习, 自适应迁移学习, 训练图像