CRESCENT Technical Short Course: Machine Learning (May 12-14, 2025)

[Shannon Fasola]

CRESCENT Technical Short Course – Machine Learning 

 

Date: May 12 - 14, 2025

Location: University of Washington – Seattle, Washington

 

This in-person three-day short course provides a hands-on introduction to machine learning techniques for seismic event analysis. Participants will learn to develop AI-aided earthquake catalogs through three key steps: event detection, association, and location with quality control. The workshop will include a mix of presentations and hands-on tutorials.

 

By the end of this short course, participants will be able to: 

• Explain the role of machine learning in earthquake detection, association, and location. 
• Select appropriate neural network architectures for earthquake detection and phase picking. 
• Train models using labeled seismic datasets and evaluate their performance. 
• Implement trained models to detect and associate seismic events in real-world data. 
• Optimize model parameters for accuracy and efficiency in earthquake cataloging. 
• Integrate machine learning outputs into earthquake location algorithms. 
• Assess model predictions and refine event catalogs through quality control methods. 
• Design end-to-end machine learning workflows tailored to specific seismic networks or research needs. 
• Collaborate on participant-led exercises to improve catalog quality and reliability. 

 

This workshop is for graduate students, postdocs, and professionals in the field of earthquake science who have specific research or application needs for machine learning in earthquake detection, association, and/or location. They should have intermediate python skills and a current interest in utilizing this technology.

 

We will cover travel, 4 nights of accommodations, including breakfast and lunch during the conference.

 

For more information, see the short course’s website.

 

Application Deadline: March 15, 2025 (CLICK TO APPLY). Space is limited to 20 participants.

 

Email GEI Program Manager Shannon Fasola (sfa...@uoregon.edu) with any questions.