Los Alamos Lab Research Findings Could Lead to Earthquake Early Warning System


Machine-learning research published in two related papers today in Nature Geosciences reports the detection of seismic signals accurately predicting the Cascadia fault’s slow slippage, a type of failure observed to precede large earthquakes in other subduction zones.
Researchers at the Los Alamos National Laboratory believe that careful monitoring in Cascadia can provide new information that can be used to provide early warning systems based on their findings.
The Cascadia area is a 700-mile-long property from southern California to northern California, which has strained cities from northern California.

The researchers applied machine learning to analyze Cascadia data and discovered a continuous chills broadcast of megathrust publications, a fingerprint that displaced the error. More importantly, they found a direct parallel between the noise and the physical changes of the acoustic signal of the error.

The moans of Cascadia, which had previously been downloaded as meaningless noise, had already predicted their fragility.

Dü Cascadia’s behavior is embedded in the data. Until the machine revealed precise patterns, we took the continuous signal as noise, but it was full of rich information. , We discovered a highly predictable sound pattern that shows slip and failure failure, Los said Los Alamos scientist Paul Johnson. ”We also found a precise link between the fragility of the error and the power of the signal, which can help us better predict a megaquake.“

The new paper was written by Johnson, Bertrand Rouet-Leduc from the Department of Earth and Environmental Sciences of the Laboratory, and by Claudia Hulbert from the Department of Earth and Environmental Sciences at the Laboratory, Christopher Ren from the Department of Intelligence and Space Research at the Laboratory and partners at the Pennsylvania State University.

Last year, the team simulated an earthquake in the lab using steel blocks that interacted with rocks and pistons, and recorded the sounds they analyzed with machine learning. They discovered that a large number of seismic signals, which had previously been reduced as meaningless noise, indicated that when the simulated error would shift, there was a great improvement in earthquake prediction. Faster, stronger earthquakes had higher signals.

The team decided to implement their new paradigm into the real world: Cascadia, whose latest research is active, said the events seemed random. This team analyzed the 12-year real data from the seismic stations in the region and found similar signals and results: Continuous vibrations of Cascadia measure the displacement of the slow sliding part of the subduction zone. In the laboratory, the authors identified a similar signal that correctly predicted a wide error interval.

The Los Alamos National Laboratory is a multidisciplinary research institution dealing with strategic sciences in the name of national security. Battelle Memorial Institute, Texas A & M is operated by the Rectors of the University of California for the National System of Nuclear Safety Administration of the University System and Energy.

Legal warning !
The information, comments and suggestions there are not covered by investment advice. It is based on the author's personal opinions. These views may not fit your financial situation and risk and return preferences. For this reason, based solely on this information, investment decisions may not have the appropriate consequences for your expectation. Our Site is not responsible for any direct or indirect damages incurred by the investors as a result of the use of the information on the Site, deficiencies in the sources, damages incurred by profit, moral damages, or damage to third parties.