2025 CRESCENT Machine Learning Technical Short Course#
Program Overview#
This 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 course covers neural network architecture selection, model training, performance metrics, and application to continuous seismic data. The workshop will include a mix of presentations and hands-on tutorials. The final day will include a participant hack-a-thon in which students attempt to develop a machine learning based quality control workflow to apply to future generations of machine learning earthquake catalogs.
Learning Goals and Objectives#
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.
Agenda#
Time |
Day 1 (Mon) |
Day 2 (Tue) |
Day 3 (Wed) |
|---|---|---|---|
8:30 – 9:00am |
Overview Talk: Intro to AI in Seismology (Marine Denolle) |
Research Talk: Detecting LFEs with a CNN in Cascadia (Amanda Thomas) |
Research Talk: QC Part II (Nate Stevens) |
9:00 – 10:30am |
Research Talk: AI-ready Data Set for the Pacific Northwest, Notebook (Yiyu Ni) |
Lecture: Training a Graph Network (Loic Bachelot) |
Research Talk: Catalog Building in the age of AI (Felix Waldhauser) |
10:30 – 11:00am |
Coffee Break |
Coffee Break |
Coffee Break |
11:00 – 12:30pm |
Lecture: Training a Phase Picker,Notebook (Loïc Bachelot) |
Research talk: Intro to Association (Ian McBrearty) |
Hackathon: Event Relocations with HypoDD, Notebook (Felix Waldhauser) |
12:30 – 1:30pm |
Lunch |
Lunch |
Lunch |
1:30 – 2:30pm |
Lecture: More on training a Phase Picker (Loïc Bachelot) |
Research Talk: Multi-Geohazard Event Discrimination (Akash Kharita) |
Participant Lightening Talks and Wrap up |
2:30 – 3:00pm |
Lecture: Evaluating Model Performance (Amanda Thomas) |
Lecture: Quality Control and Data Wrangling (Nate Stevens) |
|
3:00 – 5:00pm |
Hackathon: Detect and Pick on continuous Data, Seisbench, Pick Database (Amanda Thomas) |
Hackathon: Quality Control Metrics (Nate Stevens) |
Prerequisites#
Participants must have intermediate python skills including:
Core Python Proficiency – Comfortable with syntax, functions, and best practices.
Data Handling – Uses pandas and NumPy for data manipulation and analysis.
Automation & File Handling – Reads/writes files, automates tasks, and web scrapes with requests.
Debugging & Exception Handling – Uses try-except, logging, and debugging tools.
Data Visualization – Creates plots using Matplotlib, Seaborn, or plotly.
Algorithms & Data Structures – Implements sorting and searching
Version Control – Works with Git/GitHub, branches, and pull requests.
Python Packages & Environments – Creates/imports modules, manages dependencies with venv/conda.
Must have a laptop computer capable of accessing the internet.
Instructors#
Marine Denolle (University of Washington)
Amanda Thomas (University of California, Davis)
Ian McBrearty (Stanford University)
Loïc Bachelot (University of Oregon)
Yiyu Ni (University of Washington)
Akash Kharita (University of Washington)
Felix Waldhauser (Columbia University)
Nate Stevens (PNSN | Unversity of Washington)