Bathymetry Reconstruction by Bayesian Inference
For oceanographers and environmental monitoring, this provides a more robust bathymetry reconstruction method with uncertainty quantification, though it is an incremental application of Bayesian inference to a specific domain.
This paper introduces a Bayesian inference method for reconstructing bathymetries from water height measurements, achieving improved normalized root mean squared error (NRMSE) and uncertainty quantification compared to adjoint optimization in a wave flume experiment.
Bathymetry reconstruction is an important problem in various fields, including oceanography and environmental monitoring. This paper presents a Bayesian inference approach to reconstructing bathymetries from point measurements of the water height. We test the method for parameterized and discretized bathymetries with synthetic data to evaluate its performance and limitations. Our results indicate that the Bayesian framework provides a robust approach to bathymetry reconstruction. Finally, we use the framework to reconstruct a real-world bathymetry in a wave flume from experimental measurements and compare its performance to an adjoint optimization method. The Bayesian approach improves the normalized root mean squared error (NRMSE) of the reconstruction and provides better qualitative features, while also quantifying uncertainty.