The training function used in the ANN method, especially backpropagation, can produce
different forecasting accuracy, depending on the method parameters given and the data to …

The simulation of seismic waves is a core task in many geophysical applications. Numerical
methods such as finite difference (FD) modelling and spectral element methods (SEMs) are …

We test a fully non-linear method to solve Bayesian seismic tomographic problems using
data consisting of observed traveltimes of first-arriving waves. Rather than using Monte …

Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …

Seismic tomography is a methodology to image the interior of solid or fluid media and is
often used to map properties in the subsurface of the Earth. In order to better interpret the …

This paper proposes a deep learning framework for artefact identification and suppression in
the context of non-destructive evaluation. The model, based on the concept of autoencoders …

Geoscientists use observed data to estimate properties of the Earth's interior. This often
requires non‐linear inverse problems to be solved and uncertainties to be estimated …

Lithology identification is an important part of reservoir evaluation and reservoir description
when processing and interpreting geophysical record data. Clustering analysis refers to the …

To estimate the spatial distribution of porosity, model-driven or data-driven methods are
usually used to establish the relationship between porosity and seismic elastic parameters …

Estimating the spatially varying microstructures of heterogeneous and locally anisotropic
media non-destructively is necessary for the accurate detection of flaws and reliable …