Understanding the role of connectivity for the characterization of heterogeneous porous
aquifers or reservoirs is a very active and new field of research. In that framework …

Applications of graph theory have proliferated across the academic spectrum in recent
years. Whereas geosciences and landscape ecology have made rich use of graph theory, its …

Probabilistic inversion within a multiple‐point statistics framework is often computationally
prohibitive for high‐dimensional problems. To partly address this, we introduce and evaluate …

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 …

The stochastic models and deep‐learning models are the two most commonly used
methods for subsurface sedimentary structures identification. The results from the stochastic …

Small, seasonal lakes that exist in floodplains are rarely investigated, and yet they play an
important role in the protection of biodiversity and are highly susceptible to modification due …

Abstract The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation
algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to …

A new low-dimensional parameterization based on principal component analysis (PCA) and
convolutional neural networks (CNN) is developed to represent complex geological models …

Connectivity represents one of the fundamental properties of a reservoir that directly affects
recovery. If a portion of the reservoir is not connected to a well, it cannot be drained …

Geological parameterization enables the representation of geomodels in terms of a
relatively small set of variables. Parameterization is therefore very useful in the context of …