*Invited Presentation will be given by Dr. Pui Anantrasirichai of the University of Bristol, UK*

The past few years have seen an increase in the application of machine learning methods for geophysical data analysis. This is due to the increased adoption and visibility of freely available and easy-to-use machine learning toolkits, faster computation, reduced cost of data storage, and the very large sets of continuous geophysical and laboratory experimental data. The combination of these factors means that now is the time to consider machine learning as one of the key tools in both improving routine data processing and better understanding the underlying solid-earth processes.

In this session, we welcome machine-learning focused presentations covering topics such as seismic waveform processing, earthquake cataloging, earthquake classification, earthquake cycle behavior from numerical and laboratory experiments, computer vision approaches to tectonic and volcanic monitoring, and geodynamic modelling. We also welcome abstracts from related geophysical fields that use similar data, such as from near surface processes and geophysical hazards (e.g. rockslides, avalanches, etc.).