QuakeCast: Earthquake Forecasting Using Machine Learning to Identify and Classify Preseismic Ionospheric Anomalies as Earthquake Signals: FY21 Homeland Protection and Air Traffic Control Technical Investment Program
Abstract
Earthquakes are deadly, expensive, and unpredictable. To date, the study of earthquakes has been predominantly through seismology, which has enabled researchers to describe the long-term progression of earthquakes, but is unable to provide early forecasting necessary for short-term disaster preparation. Global Navigation Satellite Systems (GNSS) satellites offer a new opportunity to study earthquakes and begin to unravel the complex interactions between preseismic processes and the global electric circuit that may be the key to earthquake early forecasting. The QuakeCast project is developing a novel machine learning method to identify ionosphere anomalies as preseismic earthquake signals and uses them to predict impending magnitude, geographical region, and timing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 20, 2022
- Accession Number
- AD1183107
Entities
People
- Bhavani Ananthabhotla
- Dieter W. Schuldt
- Jeffrey Liu
- Jessica D. Reid
- Matthew L. Weiss
- Samuel Scheele
Organizations
- Massachusetts Institute of Technology