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CONCEPT Cited by 1 source

Earthquake Early Warning (EEW)

Earthquake Early Warning (EEW) is the class of real-time systems that detect the onset of a seismic event near its source, estimate its parameters (epicentre, magnitude, expected shaking intensity at remote locations), and push alerts to users in the expected path of shaking before the damaging S-waves reach them.

The warning-budget substrate is physical: P-waves (pressure waves, ~6 km/s) travel faster than S-waves (shear waves, ~3.5 km/s), so a detector network that triggers on the P-wave has seconds-to-tens-of-seconds to issue alerts before the slower but more-damaging S-wave arrives at a given distance from the epicentre. The warning budget grows linearly with distance from the epicentre; users directly over the rupture get effectively zero warning.

EEW systems are a canonical instance of the real-time speed-vs-accuracy trade-off: early in an event, sensor data is sparse and parameter estimates are noisy; waiting for the estimate to converge directly subtracts from the warning time available to users in the path of the shaking. False alarms (over-estimated magnitude) erode the public trust the alert channel depends on; missed alarms (under-estimated magnitude) leave people in the danger zone unwarned. The trade-off is asymmetric and cumulative across many events.

Canonical wiki instance

Android Earthquake Alerts System (AEA) — Google's planet-scale EEW running on the Android device fleet. Uses consumer-smartphone accelerometers as a distributed seismometer network rather than purpose-built seismic stations. The 2025-07-17 Google Research post reports MAE of first magnitude estimate improving from 0.50 → 0.25 (moment-magnitude units) over the preceding three years, with accuracy "similar to and in some cases better than" established traditional seismic networks at the first-estimate step (Source: sources/2025-07-17-google-android-earthquake-alerts).

Primitives

  • Sensor fleet — either purpose-built seismic stations (USGS ShakeAlert, Japan's JMA, Mexico's SASMEX, Italy's INGV) or commodity consumer devices (AEA's Android accelerometers). Density + distribution around likely epicentres determines how quickly the first trigger fires and how much data the first estimate has.
  • Event detection — online anomaly detection against ambient noise; threshold + spatial-cluster confirmation to reduce per-device false positives.
  • Parameter estimation — location (trilateration-like from first triggers) and magnitude (often from P-wave amplitude / frequency content; later refined from accumulated window). The load-bearing numerical surface: magnitude drives the distance-attenuation model that decides who should be alerted.
  • Shaking-intensity prediction — per-location expected shaking intensity from (magnitude, location, local-geology), evaluated against an alert threshold.
  • Alert routing — geo-scoped push notification to devices in the predicted-shaking region, with per-user latency budget ≤ the predicted S-wave arrival time at their location.

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