Earthquake Wrangler

Earthquakes are devastating natural phenomena and generate secondary hazards such as tsunamis, landslides and fires. Their catastrophic impacts span both developed nations including the United States, Japan, Turkey, and Italy and developing countries such as El Salvador, Haiti, Nepal and the Philippines, where disparities in early warning infrastructure remain important. Seismic events start with stress waves generated by tectonic plate motion, with body waves (P- waves and S-waves) and surface Rayleigh and love waves that carry energy through the earth. While some regions have adopted advanced early warning systems based on seismic hazard models and strong ground motion analysis, others lack the technical and economic capacity to deploy these life saving measures. This thesis presents Earthquake Wrangler, an IOS Based earthquake early warning system that leverages smartphone sensors for real time distributed detection. The system processes accelerometer and GPS data through signal filtering, dynamic thresholding and frequency analysis to identify seismic activity and deliver rapid alerts. Preliminary results show detection latency and effective discrimination of seismic events from background noise. By mobilizing widely available smartphone technology, Earthquake Wrangler offers complement to traditional seismic networks bridging critical gaps in early global warning accessibility.