581 citations · 687 across the 8 of their papers we have counts for
9 papers
Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks
Lukas Mosser
To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying process history is known. Furth…
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications
Lukas Mosser, Ehsan Zabihi Naeini
Deep neural networks offer numerous potential applications across geoscience, for example, one could argue that they are the state-of-the-art method for predicting faults in seismi…
Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
Andrea Gayon-Lombardo, Lukas Mosser, Nigel P. Brandon +1
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolut…
DeepFlow: History Matching in the Space of Deep Generative Models
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
The calibration of a reservoir model with observed transient data of fluid pressures and rates is a key task in obtaining a predictive model of the flow and transport behaviour of…
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
We present an application of deep generative models in the context of partial-differential equation (PDE) constrained inverse problems. We combine a generative adversarial network…
Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks
Lukas Mosser, Wouter Kimman, Jesper Dramsch +3
Traditional physics-based approaches to infer sub-surface properties such as full-waveform inversion or reflectivity inversion are time-consuming and computationally expensive. We…