Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

Transcranial ultrasound therapy is increasingly used for the non-invasive treatment of brain
disorders. However, conventional numerical wave solvers are currently too computationally …

Deep learning (DL) seismic simulations have become a leading-edge field that could
provide an effective alternative to traditional numerical solvers. We have developed a small …

We explore the potential of neural networks in approximating the action of the
computationally expensive Estimation of Primaries by Sparse Inversion (EPSI) algorithm …

We propose a deep learning approach for wave propagation in media with multiscale wave
speed, using a second-order linear wave equation model. We use neural networks to …

Solving the Helmholtz wave equation provides wavefield solutions that are dimensionally
compressed, per frequency, compared to the time domain, which is useful for many …

Uncertainty quantification is essential when dealing with ill-conditioned inverse problems
due to the inherent nonuniqueness of the solution. Bayesian approaches allow us to …

In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of
the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a …

An attractive feature of finite-difference modeling in the frequency domain is the low
recomputation cost to simulate seismic waves for many sources through the same velocity …

Accurate forward modeling is important for solving inverse problems. An inaccurate wave-
equation simulation, as a forward operator, will offset the results obtained via inversion. In …