Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network
Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network
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DOI：
10.1029/2019wr024833
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发表时间：
2019-08
影响因子：
5.4
通讯作者：
Philipp J. Kreyenberg;H. H. Bauser-H.;K. Roth
中科院分区：
文献类型：
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作者：
Philipp J. Kreyenberg;H. H. Bauser-H.;K. Roth
机器学习的最新进展为更深入地了解水文系统提供了新的机会，其中一些相关的系统数量仍然难以测量。我们使用在物理过程的数值模拟上训练的深度学习方法来探索缩小缺失系统量的信息差距的可能性。作为一个说明性的例子，我们研究了密度驱动溶质运移的数值和实验室实验中的速度场估计。使用溶质浓度分布的高分辨率观测，我们证明了该方法在结构上结合物理过程的表示的能力。对于可变浓度边界条件和均匀浓度边界条件的合成数据的速度场估计显示出相同的结果。这种能力是显著的，因为只有后者被用于训练网络。将该方法应用于Hele-Shaw池中密度驱动溶质迁移的浓度分布测量，使得在实验中可以评估速度场。这种速度场的可评估性甚至适用于密度指之间的溶质浓度可以忽略不计的区域，在该区域中，速度场是不可接近的。
Recent advances in machine learning open new opportunities to gain deeper insight into hydrological systems, where some relevant system quantities remain difficult to measure. We use deep learning methods trained on numerical simulations of the physical processes to explore the possibilities of closing the information gap of missing system quantities. As an illustrative example we study the estimation of velocity fields in numerical and laboratory experiments of density‐driven solute transport. Using high‐resolution observations of the solute concentration distribution, we demonstrate the capability of the method to structurally incorporate the representation of the physical processes. Velocity field estimation for synthetic data for both variable and uniform concentration boundary conditions showed equal results. This capability is remarkable because only the latter was employed for training the network. Applying the method to measured concentration distributions of density‐driven solute transport in a Hele‐Shaw cell makes the velocity field assessable in the experiment. This assessability of the velocity field even holds for regions with negligible solute concentration between the density fingers, where the velocity field is otherwise inaccessible.