The feasibility of data based machine learning applied to ultrasound tomography is studied
to estimate water-saturated porous material parameters. In this work, the data to train the …

Convolutional neural networks can provide a potential framework to characterize
groundwater storage from seismic data. Estimation of key components, such as the amount …

In this paper, we consider a high-order discontinuous Galerkin (DG) method for modelling
wave propagation in coupled poroelastic–elastic media. The upwind numerical flux is …

Photoacoustic tomography is an imaging modality based on the photoacoustic effect caused
by the absorption of an externally introduced light pulse. In the inverse problem of …

We present a new methodology of the finite-difference (FD) modelling of seismic wave
propagation in a strongly heterogeneous medium composed of poroelastic (P) and (strictly) …

Nondestructive damage identification is a central task in industrial applications in, for
example, aeronautical, civil and naval engineering. The identification approaches based on …

Time harmonic inverse scattering using accurate forward models is often computationally
expensive. On the other hand, the use of computationally efficient solvers, such as the Born …

The use of accurate computational models for damage identification problems may lead to
prohibitive costs. Damage identification problems are often characterized as inverse ill …

We develop a numerical solver for three-dimensional poroelastic wave propagation, based
on a high-order discontinuous Galerkin (DG) method, with the Biot poroelastic wave …

Nondestructive damage identification is a central task, for example, in aeronautical, civil and
naval engineering. The identification approaches based on (physical) models rely on the …