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Smartphones can function as early warning systems for large earthquakes, says a study.
The study found that the Global Positioning System receivers in a smartphone can detect the permanent ground movement (displacement) caused by fault motion in a large earthquake.
Using crowd-sourced observations from participating users' smartphones, earthquakes could be detected, analysed, and customised earthquake warnings could be transmitted back to users, the researchers observed.
"Crowd-sourced alerting means that the community will benefit by data generated from the community," said lead author Sarah Minson from U.S. Geological Survey (USGS).
Earthquake early warning systems detect the start of an earthquake and rapidly transmit warnings to people and automated systems before they experience shaking at their location.
Researchers tested the feasibility of crowd-sourced EEW with a simulation of a hypothetical magnitude seven earthquake, and with real data from the 2011 magnitude nine Tohoku-oki, Japan earthquake.
The results showed that crowd-sourced EEW could be achieved with only a tiny percentage of people in a given area contributing information from their smartphones.
For example, if phones from fewer than 5000 people in a large metropolitan area responded, the earthquake could be detected and analysed fast enough to issue a warning to areas farther away before the onset of strong shaking.
However, smartphone sensors could sense earthquakes of magnitude seven or larger, but not smaller yet potentially damaging quakes.
"Crowd-sourced data are less precise, but for larger earthquakes that cause large shifts in the ground surface, they contain enough information to detect that an earthquake has occurred, information necessary for early warning," said study co-author Susan Owen from NASA's Jet Propulsion Laboratory, California.
This technology could serve regions of the world that cannot afford conventional earthquake early warning systems, which are very expensive.
"Most of the world does not receive earthquake warnings mainly due to the cost of building the necessary scientific monitoring networks," said project lead Benjamin Brooks, also from USGS.
The study was published in Science journal.