Space Seismic Analysis
A data science project analyzing seismic data from planetary missions. Applies signal processing techniques and machine learning models to detect and classify seismic events in extraterrestrial datasets provided by NASA.
Problem Statement
Planetary seismic data contains significant noise and requires specialized analysis techniques different from terrestrial seismology. Manual analysis of large datasets is impractical for ongoing planetary missions.
Solution
Built an automated pipeline that applies bandpass filtering, STA/LTA algorithms, and ML-based event classification to detect and categorize seismic events in planetary datasets. Includes comprehensive visualization for scientific interpretation.
Key Features
- ▸Automated seismic event detection using STA/LTA algorithm
- ▸Bandpass filtering for noise reduction
- ▸ML-based event classification (earthquake, impact, noise)
- ▸Interactive visualizations for scientific analysis
- ▸Batch processing of large seismic datasets
- ▸Statistical analysis and event catalog generation
Challenges
- ⚡Extremely low signal-to-noise ratio in planetary seismic data
- ⚡Limited labeled training data for extraterrestrial seismic events
- ⚡Distinguishing between geological and instrumental artifacts
- ⚡Handling different data formats from various missions
Results & Metrics
Successful detection of seismic events in NASA lunar dataset
85%+ classification accuracy for event type determination
Automated processing of months of continuous seismic recordings
Reproducible analysis pipeline with comprehensive documentation
Lessons Learned
- 💡Signal processing fundamentals are essential for scientific ML applications
- 💡Domain expertise collaboration is critical for meaningful results
- 💡Visualization is crucial for validation in scientific computing
Case Study Overview
Case Study: Space Seismic Analysis
Planetary data science and ML pipeline using signal processing to detect and categorize seismic events from Apollo missions.