Modeling of heterogeneous materials and media is a problem of fundamental importance to
a wide class of phenomena and systems, ranging from condensed matter physics, soft …

Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …

Mineral image segmentation plays a vital role in the realization of machine vision based
intelligent ore sorting equipment. However, the existing image segmentation methods still …

Abstract We present the PoreFlow-Net, a 3D convolutional neural network architecture that
provides fast and accurate fluid flow predictions for 3D digital rock images. We trained our …

Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …

In hydrocarbon reservoir evaluation, lithology is a key characteristic for determination of
storage capacity and rock properties. Lithology is usually predicted from well log data or …

Multilevel thresholding has got more attention in recent years with various successful
applications. However, the implementation becomes more and more complex and time …

Similar to many fields of sciences, recent deep learning advances have been applied
extensively in geosciences for both small-and large-scale problems. However, the necessity …

Porous media are ubiquitous in both nature and engineering applications. Therefore, their
modeling and understanding is of vital importance. In contrast to direct acquisition of three …

Digital rock technology is becoming essential in reservoir engineering and petrophysics.
Three-dimensional digital rock reconstruction, image resolution enhancement, image …