BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …

Quantitative predictions of natural and induced phenomena in fractured rock is one of the
great challenges in the Earth and Energy Sciences with far‐reaching economic and …

This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

The arrangements of particles and forces in granular materials have a complex organization
on multiple spatial scales that range from local structures to mesoscale and system-wide …

Earthquake prediction, the long-sought holy grail of earthquake science, continues to
confound Earth scientists. Could we make advances by crowdsourcing, drawing from the …

Deep learning techniques are increasingly being considered for geological applications
where—much like in computer vision—the challenges are characterized by high …

The durability of rocks is a substantial rock property that has to be considered for designing
geotechnical structures. Uniaxial compressive strength (UCS) and Young's modulus (E) are …

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become
an indispensable component in achieving the goal of the net-zero carbon economy, given its …

We propose a machine learning approach to address a key challenge in materials science:
predicting how fractures propagate in brittle materials under stress, and how these materials …

Flow channelization is a commonly observed phenomenon in fractured subsurface media
where the flow of fluids is restricted primarily to highly transmissive fracture networks …