Our team brings together a diverse set of backgrounds and skills, including two South Korean military veterans with leadership and problem-solving expertise and a hearing-impaired member providing valuable insights on accessibility and adaptability. United by our shared interest in projects that make a positive social impact, we approach challenges from multiple angles, leveraging our varied perspectives and experiences to achieve common goals. We offer a welcoming, communicative environment where new members can learn, grow, and collaborate with a motivated, adaptable, and mission-driven team.
Planetary seismology missions struggle with the power requirements necessary to send continuous seismic data back to Earth. But only a fraction of this data is scientifically useful! Instead of sending back all the data collected, what if we could program a lander to distinguish signals from noise, and send back only the data we care about? Your challenge is to write a computer program to analyze real data from the Apollo missions and the Mars InSight Lander to identify seismic quakes within the noise!
Detailed information about this project can be found in the link below.
Detecting seismic activity on Mars and the Moon is essential for understanding planetary geology and enhancing space exploration. Our goal is to develop a model to identify seismic anomalies in planetary data to identify earthquakes. We also want to determine the patterns of seismicity on the Moon and Mars that we hypothesize can be divided into foreshocks, mainshocks, and aftershocks similar to Earth.
For Lunar data, the data is relatively sufficient than Mars. For Mars data, we used IQR Method to overcome the problem of insufficient data. We trained scalar function to scale the datasets before we put them into our model.
We used LSTM Autoencoder to detect anomalies in time series data.
We visualized the detected anomalies in a graph, and highlighted the anomalies with a red marker for analysis.
We outputted the CSV files from the data that detected anomalies on the graph.
- Our analysis revealed patterns in lunar and Martian seismic activities that appear to follow Earth-like sequences of foreshocks, mainshocks, and aftershocks
- The detected patterns, while similar in sequence, showed unique characteristics specific to each celestial body
- Benefited from relatively sufficient data compared to Mars
- Successfully applied LSTM Autoencoder without requiring additional data processing techniques
- Clear pattern recognition achieved through comprehensive dataset
- Multiple seismic sequences identified through anomaly detection
- Reconstruction MAE showed expected right-skewed distribution
- Threshold determination proved effective for identifying significant events
- Successfully implemented IQR method to address data insufficiency
- Maintained data integrity while generating usable training sets
- Scaled datasets effectively before model implementation
- Plot 1 demonstrated a medium-intensity precursor, followed by a major event and smaller subsequent activity
- Plot 2 revealed a sequence of three minor events preceding a major seismic event, followed by continued vibrations
- Pattern consistency suggests reliable detection despite limited data
- Successfully developed and implemented LSTM Autoencoder for planetary seismic detection
- Created an effective framework for analyzing limited datasets through IQR method
- Established reliable threshold determination process for anomaly detection
- Confirmed the presence of three-phase seismic patterns on other planetary bodies
- Demonstrated the effectiveness of machine learning in extraterrestrial seismic detection
- Identified unique characteristics of planetary seismic events
- Expand training dataset size beyond hackathon limitations
- Enhance model performance through extended testing periods
- Develop more sophisticated pattern recognition algorithms
- Further investigation of detected patterns across longer time periods
- Comparative analysis of seismic characteristics between Earth, Moon, and Mars
- Integration with other planetary data sources
- Despite time constraints of the NASA Space Apps Challenge, we successfully developed a functional seismic detection algorithm
- Our findings suggest promising applications for planetary exploration and geological understanding
- The methodology provides a foundation for future research in extraterrestrial seismic activity detection