Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry. (December 2022)

• Record Type: Journal Article
• Authors: Forghani, Mojtaba; Qian, Yizhou; Lee, Jonghyun; Farthing, Matthew; Hesser, Tyler; Kitanidis, Peter K.; Darve, Eric F.
• Is Part Of: Advances in water resources. Volume 170(2022)
• Publication Date: 2022-12
• Subjects: Deep learning -- Inverse modeling -- Reduced-order models -- Riverine bathymetry -- Variational encoder -- Bayesian estimation; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2022.104323
• Languages: English
• ISSNs: 0309-1708

Abstract

Abstract: Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. The reformulation allows variational inference with a small number (e.g., O ( 100 ) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.