Scientists can predict where an earthquake might occur, but predicting when it will occur and how strong it will be has been an intractable challenge. A new artificial intelligence-based method identifies sounds that indicate when a fault is about to rupture. An earthquake occurs when massive blocks of Earth, often near the interface between tectonic plates, suddenly slip along fractures in the earth, or faults. The same stress that holds the rock in place under pressure--friction--builds up to a point that the rocks slip past one another rapidly and forcefully, releasing energy via seismic waves. Los Alamos National Laboratory researchers and colleagues discovered a way to successfully predict earthquakes in a laboratory experiment that simulates natural conditions.

Scientists have developed an artificial intelligence (AI) system to successfully predict earthquakes, an advance that may help prepare for natural disasters and potentially save lives. The study, published in the journal Geophysical Review Letters, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Researchers from University of Cambridge in the UK and Boston University in the US studied the interactions among earthquakes, precursor quakes and faults, with the hope of developing a method to predict earthquakes. Using a lab-based system that mimics real earthquakes, they used machine learning techniques to analyse the acoustic signals coming from the'fault' as it moved and search for patterns. Researchers used steel blocks to closely mimic the physical forces at work in a real earthquake, and also records the seismic signals and sounds that are emitted.

Scientists have developed an artificial intelligence (AI) system to successfully predict earthquakes, an advance that may help prepare for natural disasters and potentially save lives. The study, published in the journal Geophysical Review Letters, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Researchers from University of Cambridge in the UK and Boston University in the US studied the interactions among earthquakes, precursor quakes and faults, with the hope of developing a method to predict earthquakes. Using a lab-based system that mimics real earthquakes, they used machine learning techniques to analyse the acoustic signals coming from the'fault' as it moved and search for patterns. Researchers used steel blocks to closely mimic the physical forces at work in a real earthquake, and also records the seismic signals and sounds that are emitted.

A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake. The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal.

A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake. The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal.

Earthquakes occurred here in 1857, 1881, 1901, 1922, 1934, and 1966, suggesting a pattern in which quakes occur every 22 years give or take a few years. Geologists know that as a quake approaches, the gouge material begins to fail, emitting groans and cracks as it shears--a kind of seismic chatter. "We show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy," they say. The first and most obvious question it raises is whether the same technique could predict real earthquakes accurately.

Geologists have long been able to work out the approximate risk of an earthquake. Their approach is to work out when the fault moved in the past and use any periodicity to predict the future. The most famous example involves the Parkfield segment of the San Andreas Fault in California, one of the most carefully studied faults on the planet. Earthquakes occurred here in 1857, 1881, 1901, 1922, 1934, and 1966, suggesting a pattern in which quakes occur every 22 years give or take a few years. Geologists therefore predicted that a quake would occur between 1988 and 1993, but they had to wait until 2004 for their temblor.

Geologists have long been able to work out the approximate risk of an earthquake. Their approach is to work out when the fault moved in the past and use any periodicity to predict the future. The most famous example involves the Parkfield segment of the San Andreas Fault in California, one of the most carefully studied faults on the planet. Earthquakes occurred here in 1857, 1881, 1901, 1922, 1934, and 1966, suggesting a pattern in which quakes occur every 22 years give or take a few years. Geologists therefore predicted that a quake would occur between 1988 and 1993, but they had to wait until 2004 for their temblor.