389 citations · 1.5k across the 24 of their papers we have counts for
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Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Eric Laloy, Romain Hérault, John Lee +2
Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational a…
Training-image based geostatistical inversion using a spatial generative adversarial neural network
Eric Laloy, Romain Hérault, Diederik Jacques +1
Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and ev…
Inferring transport characteristics in a fractured rock aquifer by combining single-hole GPR reflection monitoring and tracer test data
C. Dorn, N. Linde, T. Le Borgne +2
Investigations of solute transport in fractured rock aquifers often rely on tracer test data acquired at a limited number of observation points. Such data do not, by themselves, al…
Distributed soil moisture from crosshole ground-penetrating radar travel times using stochastic inversion
N. Linde, J. A. Vrugt
Geophysical methods offer several key advantages over conventional subsurface measurement approaches, yet their use for hydrologic interpretation is often problematic. Here, we int…
Hydrogeophysical characterization of transport processes in fractured rock by combining push-pull tracer tests and single-hole ground penetrating radar
A. Shakas, N. Linde, L. Baron +4
The in situ characterization of transport processes in fractured media is particularly challenging due to the considerable spatial uncertainty on tracer pathways and dominant contr…
Single-hole GPR reflection imaging of solute transport in a granitic aquifer
C. Dorn, N- Linde, T. Le Borgne +2
Identifying transport pathways in fractured rock is extremely challenging as flow is often organized in a few fractures that occupy a very small portion of the rock volume. We demo…