Sydney, Dec 7 (IANS) Researchers have pinpointed the hotspots where the world's largest earthquakes are most likely to occur with greater accuracy than ever before.
"Subduction zones, where one plate slips under another, have long been known to harbour very powerful earthquakes but our research suggests that regions where fracture zones on the seafloor meet subduction zones are at much higher risk," said Dietmar Muller, professor at the University of Sydney's School of Geosciences.
"The advantage of our new method is that it picks up many of the regions prone to recurring powerful earthquakes over long time periods, including some where no large earthquakes have occurred in the last 100 or so years. Our results could contribute to much-needed improvements of long-term seismic hazard maps," said Muller.
Fracture zones are like rail tracks on the sea floor, tracking the history of plate motions and often tied to enormous submarine ridges elevated by up to three kilometres above the surrounding abyssal plains, the journal Solid Earth reported.
The Pacific 'ring of fire', an area of high earthquake and volcanic activity, and other regions where two tectonic plates converge, are sites for some of the world's largest earthquakes, according to a Sydney statement.
"We found that 87 percent of the 15 largest (8.6 magnitude or higher) and half of the 50 largest (8.4 magnitude or higher) earthquakes of the past century are associated with areas of intersection between oceanic fracture zones and subduction zones," said Muller, who led the research with Thomas Landgrebe, also from the School of Geosciences.
The coasts of Southern Chile and Peru, Indonesia's Sumatra Island, and several regions along the eastern Eurasian coastline, are some of the regions prone to great earthquakes.
The researchers considered about 1,500 earthquakes in their study.
They used geophysical data, mapping fracture zones and subduction zones, and a database of significant post-1900 events.
They analysed the information by applying a recently developed data mining method previously only used to match internet users to consumer goods.
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