24 citations · 56 across the 9 of their papers we have counts for
10 papers
Sequential Physics-Constrained Neural Operator Forward Modeling for the Reservoir System
Clement Etienam, Juntao Yang, Oleg Ovcharenko +4
We develop a comprehensive mathematical and computational framework for sequential surrogate modeling of three-phase black-oil reservoir dynamics using neural operators, with parti…
Plug-and-Play regularized 3D seismic inversion with 2D pre-trained denoisers
Nick Luiken, Juan Romero, Miguel Corrales +1
Post-stack seismic inversion is a widely used technique to retrieve high-resolution acoustic impedance models from migrated seismic data. Its modelling operator assumes that a migr…
IntraSeismic: a coordinate-based learning approach to seismic inversion
Juan Romero, Wolfgang Heidrich, Nick Luiken +1
Seismic imaging is the numerical process of creating a volumetric representation of the subsurface geological structures from elastic waves recorded at the surface of the Earth. As…
Laterally constrained low-rank seismic data completion via cyclic-shear transform
David Vargas, Ivan Vasconcelos, Nick Luiken +1
A crucial step in seismic data processing consists in reconstructing the wavefields at spatial locations where faulty or absent sources and/or receivers result in missing data. Sev…
Why POCS works, and how to make it better
Matteo Ravasi, Nick Luiken
Projection Over Convex Sets (POCS) is one of the most widely used algorithms in geophysical data processing to interpolate seismic data. Whilst usually described as a modification…
Seeing through the CO2 plume: joint inversion-segmentation of the Sleipner 4D Seismic Dataset
Juan Romero, Nick Luiken, Matteo Ravasi
4D seismic inversion is the leading method to quantitatively monitor fluid flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2 s…
Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion
Muhammad Izzatullah, Tariq Alkhalifah, Juan Romero +3
Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion…
A hybrid approach to seismic deblending: when physics meets self-supervision
Nick Luiken, Matteo Ravasi, Claire E. Birnie
To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous sh…
Stochastic Multi-Dimensional Deconvolution
Matteo Ravasi, Tamil Selvan, Nick Luiken
Seismic datasets contain valuable information that originate from areas of interest in the subsurface; such seismic reflections are however inevitably contaminated by other events…
Relaxed regularization for linear inverse problems
Nick Luiken, Tristan van Leeuwen
We consider regularized least-squares problems of the form . Recently, Zheng et al., 2019, proposed an algorithm called…