Tectonic shifts generate seismic waves that ripple through the Earth's crust. The first waves of these earthquakes are primary waves, and are the least damaging. The secondary waves, or S-waves, that follow are far more destructive. Fast detection of primary waves can serve as a warning for S-waves, and are essential for early warning systems.
Seismologists use seismometers and accelerometers to measure ground movement and vibrations. These instruments reveal an earthquake's strength, location, and how far its effects will spread, enabling authorities to make informed decisions for public protection.1,2
Building on these foundations, modern technologies are extending detection in ways once thought impossible. Distributed Acoustic Sensing (DAS), for instance, turns ordinary optical fibers into densely packed seismic sensors.
With fibers stretching across cities and even along the ocean floor, DAS could provide a way to monitor regions where traditional networks are sparse, adding speed and high resolution to global seismic monitoring.3,4
Motion Sensor Technologies: From Specialist to Ubiquitous
Specialist equipment and carefully chosen locations no longer limit seismic monitoring. Advances in microelectromechanical systems (MEMS) have made it possible to build accelerometers so small and cheap that they now sit inside every smartphone. These sensors can accurately track movement and vibration across a frequency range relevant for seismic detection.5
When properly calibrated, modern smartphones can detect ground vibrations as tiny as 10-4 m/s2 across a frequency range of two to 40 Hz, the sweet spot for seismic activity.
Researchers systematically evaluated smartphone accelerometers, finding that models vary in performance, but when tested against reference-grade accelerometers, all smartphones proved remarkably capable. If synchronized, they could become powerful detectors of earthquakes.5
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Further research demonstrates the power of smartphones for earthquake detection. A recent paper in Scientific Reports showed that crowdsourced earthquake detection using mobile phones can rival the performance of traditional seismometer networks for moderate-to-large events.
Another example is Google's earthquake alert system in Android phones, which has detected over 11,000 quakes and has warned millions of users in 98 countries. The collective sensing approach not only amplifies detection coverage but also enables detailed mapping of shaking intensity during emergencies, supporting coordinated response efforts.6,7
The key to making smartphone networks work is reliable software. Algorithms must distibguish between genuine seismic activity and everyday vibrations. Here, calibration and time-stamping can help eliminate false positives. When enough devices feed into the system, alerts can reach people seconds before destructive waves arrive, enough time to duck for cover, stop a train, or pause industrial machinery. In other words, enough time to save lives.5,7
Networked Sensing: Hybrid and Synchronized Systems
The strength of distributed sensing lies in numbers. When hundreds or thousands of devices register the same ground movements at the same time, the signal output generated becomes much clearer. Synchronization protocols such as the Network Time Protocol (NTP) align these measurements to the millisecond, enabling near instant analysis and filtering.
Hybrid networks that combine scientific-grade accelerometers, experimental quantum sensors, and smartphones into one network can create the perfect system. The high-fidelity instruments deliver precision, while consumer devices (phones, laptops, iPads) extend coverage into places professional networks can't reach.3-5
This results in a layered system that captures strong quakes down to the subtlest tremors with impressive reliability.5
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Early Warning Systems and Public Response
Earthquake Early Warning (EEW) systems already protect lives in countries such as Japan, Mexico, and the United States. These systems combine dense networks of sensors with rapid data transmission and public communication channels, from radio broadcasts to smartphone alerts.1,7
The process is almost entirely automated. Sensors detect seismic waves, software pinpoints the quake’s origin, algorithms estimate its magnitude, and predictive models calculate how shaking will spread. All of this unfolds in seconds, allowing alerts to be issued before S-waves arrive.1,2
Studies of public response show that trust in these alerts depends heavily on reliability. When warnings are consistent and accurate, people take them seriously, responding quickly and appropriately.
Blended approaches, which combine traditional seismic networks with crowdsourced smartphone data, have expanded coverage dramatically. In areas where official monitoring stations are absent, smartphones often provide the only line of defense.6,7
Fiber Optic and Microwave Frequency-Based Sensing
Modern motion sensor technologies are pushing seismic monitoring into new territory. Fiber optic networks adapted for DAS transform exiting telecommunication cables into long strings of seismic sensors.
This is particularly valuable for monitoring the ocean floor, where conventional instruments are prohibitively expensive.
Another innovation, the Microwave Frequency Fiber Interferometer (MFFI), uses stable microwave signals transmitted through commercial fiber networks. Ground motion alters the phase of these signals, providing a direct readout of seismic activity.
Trials have shown that MFFI matches the performance of accelerometers and DAS while offering unique benefits: ranges of more than 50 kilometers, low costs under $1,000, and resistance to environmental noise. In combination, this makes MFFI attractive for both dense urban areas and remote locations.3
Both DAS and MFFI deliver real-time, high-resolution data that improve earthquake mapping and strengthen early warnings. Their adaptability and cost-effectiveness mean they are likely to play a central role in the next generation of public safety systems.3
System Limitations and Calibration Needs
Despite their advancements, motion sensors require careful calibration and noise management for reliable performance—no system is perfect.
Different sensor types and installation conditions can affect data quality. As a result, calibrating each node, synchronizing event timestamps, and continuously monitoring sensor drift or degradation are extremely important.3,5
Noise in sensing networks arises from both natural and anthropogenic sources. In cities, vibrations, temperature changes, and electromagnetic interference can lead to false signals.
While advanced processing techniques and redundancy can help reduce these issues, challenges still exist: Achieving consistent coverage across urban and rural regions while avoiding false alarms is a key success metric for future warning systems.3,5
Societal Benefits and Long-term Impact
Integrating motion sensors into earthquake detection and public warning systems has already elevated public safety standards. Ubiquitous sensing networks provide early warnings to all communities, including those lacking resources. They also help emergency responders and infrastructure operators plan evacuations and manage risks more effectively.6,7
Widespread adoption of distributed sensor networks fosters a culture of preparedness across society. When alerts are trusted and response protocols are clear, people and organizations can respond effectively, which reduces injuries, loss of life, and damage to infrastructure.
The continual refinement of motion sensor technologies and their integration into public-facing digital platforms, supported by research, engineering advancements, and thoughtful public policy, will further cultivate resilience in earthquake-prone regions, as they are becoming ever more common with climate change.
References and Further Reading
- Lin, Y., & Wu, Y. (2025). Magnitude determination for earthquake early warning using P-alert low-cost sensors during 2024 Mw7.4 Hualien, Taiwan earthquake. Scientific Reports, 15(1), 1-10. DOI:10.1038/s41598-025-97748-z. https://www.nature.com/articles/s41598-025-97748-z
- Cremen, G. et al. (2022). Investigating the potential effectiveness of earthquake early warning across Europe. Nature Communications, 13(1), 1-10. DOI:10.1038/s41467-021-27807-2. https://www.nature.com/articles/s41467-021-27807-2
- Bogris, A. et al. (2022). Sensitive seismic sensors based on microwave frequency fiber interferometry in commercially deployed cables. Scientific Reports, 12(1), 1-10. DOI:10.1038/s41598-022-18130-x. https://www.nature.com/articles/s41598-022-18130-x
- Zhu, W. et al. (2023). Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning. Nature Communications, 14(1), 1-11. DOI:10.1038/s41467-023-43355-3. https://www.nature.com/articles/s41467-023-43355-3
- Vezio, P. et al. (2024). Characterizing smartphone capabilities for seismic and structural monitoring. Scientific Reports, 14(1), 1-9. DOI:10.1038/s41598-024-72929-4. https://www.nature.com/articles/s41598-024-72929-4
- Tollefson, J. (2025). Google tapped billions of mobile phones to detect quakes worldwide — and send alerts. Nature. DOI:10.1038/d41586-025-02278-3. https://www.nature.com/articles/d41586-025-02278-3
- Finazzi, F. et al. (2024). Smartphones enabled up to 58 s strong-shaking warning in the M7.8 Türkiye earthquake. Scientific Reports, 14(1), 1-11. DOI:10.1038/s41598-024-55279-z. https://www.nature.com/articles/s41598-024-55279-z
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