The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …

Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …

Most of the geological parametrization techniques used in history matching of sub-surface
formations including the deep learning-based methods could not capture the non-linear and …

Deep learning (DL) techniques have recently attracted considerable attention in the field of
seismic data denoising. However, most DL-based seismic denoising models require a …

To improve the accuracy of the mechanical fault diagnosis of the operating mechanism and
fully exploit the characteristic information in the vibration signal of the high-voltage circuit …

The facies distribution of a reservoir is one of the biggest concerns for geologists,
geophysicists, reservoir modelers, and reservoir engineers due to its high importance in the …

The data assimilation process for geothermal reservoirs often relies on well data, which
primarily offer insights into the immediate vicinity of the borehole. However, integrating …

Inverse theory and data assimilation methods are commonly used in earth and
environmental science studies to predict unknown variables, such as the physical properties …

Numerical simulation of multiphase flow in porous media is an important step in
understanding the dynamic behavior of geologic CO 2 storage (GCS). Scaling up GCS …

The estimation and uncertainty quantification of channelized reservoirs in a data
assimilation framework is very hard to achieve due to the geometrical and topological …