Geophysical Data Science

Building reproducible Python workflows for Earth system modeling.

TL;DR

  • Problem: Help students turn geophysical data science theory into robust, reproducible code.
  • Data: Real-world geophysical datasets used in GEO4300/9300 labs and assignments.
  • Method: Hands-on Python labs + Git/GitHub workflows + iterative debugging support.
  • Output: A structured lab curriculum and a public repository of exercises/datasets.
  • Impact: Students shipped end-to-end analyses with stronger validation + reproducibility habits.
  • Links: GEO4300_2023 repository.

Situation

Geophysical data science sits at the intersection of complex physical theory and messy, real-world observations. Students often grasp the equations but struggle to translate them into robust code, facing barriers in data cleaning, dimensionality reduction, and reproducible modeling.

Task

My objective was to bridge the gap between statistical theory and applied coding. I aimed to design a lab curriculum that didn’t just teach “how to use a library,” but how to think like a data scientist—handling uncertainty, validating models, and maintaining clean, version-controlled workflows.

Action

  • Curriculum Development: Designed and deployed weekly Python labs covering the full data lifecycle:
    • Preprocessing: Handling missing data and outliers in sensor time-series.
    • Modeling: Implementing Linear/Multiple Regression, Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCA) from scratch and using libraries.
    • Advanced Analysis: Stochastic processes and frequency domain analysis (FFT) for seismic and climatic data.
  • Engineering Best Practices: Introduced students to Git/GitHub for assignment submissions, enforcing version control habits early.
  • Technical Mentorship: Provided code reviews and debugging support for course projects, guiding students through feature engineering and model selection for their specific geophysical datasets.

Result

  • Reproducible Resource: Created a structured, open-source GitHub repository that serves as a permanent reference for future cohorts.
  • Student Success: Empowered students to successfully deliver independent research projects, moving from raw data to statistical inference with confidence.
  • Bridge to Industry: The workflows taught mirrored industry standards, preparing students for roles in energy and environmental analytics.

Course Highlights

The course builds from statistical foundations to advanced machine learning applications in geoscience:

  1. Statistical Foundations: Probability distributions and hypothesis testing.
  2. Linear Models: Regression techniques and uncertainty quantification.
  3. Multivariate Analysis: Uncovering patterns with PCA and geostatistics.
  4. Time Series: Analyzing temporal dynamics in Earth systems.