Machine learning techniques are used to examine relationships between the large-scale state of the atmosphere, the convection total area, and the degree of organization in northern Australia.
machine learning & AI
Posted inNews
Monitoring Earthquakes at the Speed of Light
New research uses gravity and a machine learning model to instantaneously estimate the magnitude and location of large earthquakes.
Posted inEditors' Highlights
Machine Learning Helps See into a Volcano’s Depths
How big might future volcanic eruptions be? Crystals carry information to answer this and machine learning methods can visualize and interpret this multidimensional data.
Posted inEditors' Vox
Understanding and Utilizing the Fractured Earth
The prediction of flow and transport in fractured rock is one of the great challenges in the Earth and energy sciences with far-reaching economic and environmental impacts.
Posted inResearch Spotlights
Testing a Machine Learning Approach to Geophysical Inversion
Variational autoencoders can be leveraged to provide an effective method of inversion that is both accurate and computationally efficient.
Posted inEditors' Highlights
Corrective Machine Learning for Improving Climate Models
A machine-learned correction enables an efficient coarse-grid global atmosphere model to better track the weather and time-mean precipitation of an expensive fine-grid ‘digital twin’ reference model.
Posted inFeatures
Distributed Sensing and Machine Learning Hone Seismic Listening
Fiber-optic cables can provide a wealth of detailed data on subsurface vibrations from a wide range of sources. Machine learning offers a means to make sense of it all.
Posted inNews
Machine Learning Pinpoints Meteorite-Rich Areas in Antarctica
A new algorithm suggests that only a small fraction of meteorites present on the White Continent’s surface have been recovered to date.
Posted inResearch Spotlights
Comparing Machine Learning Models for Earthquake Detection
A new study evaluated the performance of emerging deep learning models for earthquake detection, phase identification, and phase picking.
Posted inResearch Spotlights
Neural Networks Can Identify Carbon Dioxide in Seismic Observations
By establishing a machine-driven approach to interpreting seismic observations of carbon dioxide injection, researchers hope to improve tracking of carbon capture and sequestration projects.