New machine learning techniques have estimated ocean temperatures below 2,000 meters, leading to a new model of warming trends.
Machine learning and signal processing methods offer significant benefits to the geosciences, but realizing this potential will require closer engagement among different research communities.
Machine learning is gaining popularity across scientific and technical fields, but it’s often not clear to researchers, especially young scientists, how they can apply these methods in their work.
Artificial intelligence combined with high-performance computing could trigger a fundamental change in how geoscientists extract knowledge from large volumes of data.
Satellites in the sky combined with computers on the ground detect and track volcanic ash clouds, like those produced by Soufrière St. Vincent in April, in near-real time.
Unprecedented images of fracture networks in laboratory scale experiments mixed with machine learning algorithms help predict the timing of the next failure.
Machine learning is used to retrieve global snowfall occurrence and rate from satellite-based passive microwave sounder observations, trained by snowfall data from a high-quality space borne radar.
Researchers used neural networks to better define the parameterizations necessary for modeling the distribution and characteristics of orographic gravity waves.
Tackling data challenges and incorporating physics into machine learning models will help unlock the potential of artificial intelligence to answer Earth science questions.
Classic interpretation of aeromagnetic anomaly maps involves several steps with limiting boundary conditions; a recent study develops convolutional networks largely bypassing these issues.