Reservoir simulation models are used to forecast future reservoir behavior and to optimally
manage reservoir production. These models require specification of hundreds of thousands …

Full-waveform inversion problems are usually formulated as optimization problems, where
the forward-wave propagation operator f maps the subsurface velocity structures to seismic …

I demonstrate that the conventional seismic full-waveform inversion algorithm can be
constructed as a recurrent neural network and so implemented using deep learning software …

Recent advances in machine learning and its applications in various sectors are generating
a new wave of experiments and solutions to solve geophysical problems in the oil and gas …

We design and implement quantum circuits for the simulation of the one-dimensional wave
equation on the Quantinuum H1-1 quantum computer. The circuit depth of our approach …

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 …

Full-waveform inversion (FWI) is an iterative nonlinear waveform matching procedure
subject to wave-equation constraint. FWI is highly nonlinear when the wave-equation …

Full-waveform inversion has evolved into a powerful computational tool in seismic imaging.
New misfit functions for matching simulated and measured data have recently been …

Learned graph neural networks (GNNs) have recently been established as fast and accurate
alternatives for principled solvers in simulating the dynamics of physical systems. In many …

The adjoint method is a popular method used for seismic (full-waveform) inversion today.
The method is considered to give more realistic and detailed images of the interior of the …