They started with visual representation of the 1992 magnitude 7.3 southern California Landers earthquake where the multi-colored portion represents the initial quake and the red boxes represent aftershock locations.
They applied a neural net to analyze the relationships between static stress changes caused by the mainshocks and aftershock locations. The algorithm was able to identify useful patterns.
The end result was an improved model to forecast aftershock locations and while this system is still imprecise, it’s a motivating step forward. Machine learning-based forecasts may one day help deploy emergency services and inform evacuation plans for areas at risk of an aftershock
Nature – Deep learning of aftershock patterns following large earthquakes
Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law and Omori’s law), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change is perhaps the most widely used criterion to explain the spatial distributions of aftershocks but its applicability has been disputed. Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock–aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock–aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98% of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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Let’s see… aftershocks. Insofar as historic data has shown, the Original Big One in almost all (P=0.97) of the cases contains substantially over 75% of the total event released seismic energy. The aftershocks, plural, over the following weeks-to-years (but mostly “months”) then dissipate an additional ⅓ more energy. 3 parts Big One, 1 part all the Little Ones together. Now, and again, with a bit of a tongue-in-cheek: There’s a Big One. OK boys’n’girls, what is going to follow? Aftershocks! Scary ones, at various unsettling moments of the day and night in the weeks that follow! And even with this marvel-of-retrospective-AI, the exact moment of the day or night remains a mystery. So the “being prepared for the next aftershock” is kind of mendaciously attributed to the AI team. I, a “regular I”, can also predict aftershocks. They’re agonna happen, folks. After Big Ones. And to a proportionately lesser degree, even to the smaller out-of-the-blue earthquakes. Just saying. GoatGuy
Let’s see… aftershocks. Insofar as historic data has shown the Original Big One in almost all (P=0.97) of the cases contains substantially over 75{22800fc54956079738b58e74e4dcd846757aa319aad70fcf90c97a58f3119a12} of the total event released seismic energy. The aftershocks plural over the following weeks-to-years (but mostly months””) then dissipate an additional ⅓ more energy. 3 parts Big One”” 1 part all the Little Ones together. Now and again with a bit of a tongue-in-cheek: There’s a Big One. OK boys’n’girls what is going to follow? Aftershocks! Scary ones at various unsettling moments of the day and night in the weeks that follow! And even with this marvel-of-retrospective-AI”” the exact moment of the day or night remains a mystery. So the “”””being prepared for the next aftershock”””” is kind of mendaciously attributed to the AI team. I”””” a “”””regular I”””””” can also predict aftershocks. They’re agonna happen folks. After Big Ones. And to a proportionately lesser degree”” even to the smaller out-of-the-blue earthquakes. Just saying.GoatGuy”””””””
From a planning perspective though, getting a tighter prediction of the aftershock location and strength spread might be helpful when setting up command posts/distribution points/evac stations to guide disaster recovery ops. You can set up closer, within your statistical risk tolerance, without getting so close that an aftershock overly disrupts recovery ops.
From a planning perspective though getting a tighter prediction of the aftershock location and strength spread might be helpful when setting up command posts/distribution points/evac stations to guide disaster recovery ops. You can set up closer within your statistical risk tolerance without getting so close that an aftershock overly disrupts recovery ops.
Actually the exact time and intensity would be just as important.
Actually the exact time and intensity would be just as important.
Actually the exact time and intensity would be just as important.
Actually the exact time and intensity would be just as important.
From a planning perspective though, getting a tighter prediction of the aftershock location and strength spread might be helpful when setting up command posts/distribution points/evac stations to guide disaster recovery ops. You can set up closer, within your statistical risk tolerance, without getting so close that an aftershock overly disrupts recovery ops.
From a planning perspective though getting a tighter prediction of the aftershock location and strength spread might be helpful when setting up command posts/distribution points/evac stations to guide disaster recovery ops. You can set up closer within your statistical risk tolerance without getting so close that an aftershock overly disrupts recovery ops.
Actually the exact time and intensity would be just as important.
Let’s see… aftershocks. Insofar as historic data has shown, the Original Big One in almost all (P=0.97) of the cases contains substantially over 75% of the total event released seismic energy. The aftershocks, plural, over the following weeks-to-years (but mostly “months”) then dissipate an additional ⅓ more energy. 3 parts Big One, 1 part all the Little Ones together. Now, and again, with a bit of a tongue-in-cheek: There’s a Big One. OK boys’n’girls, what is going to follow? Aftershocks! Scary ones, at various unsettling moments of the day and night in the weeks that follow! And even with this marvel-of-retrospective-AI, the exact moment of the day or night remains a mystery. So the “being prepared for the next aftershock” is kind of mendaciously attributed to the AI team. I, a “regular I”, can also predict aftershocks. They’re agonna happen, folks. After Big Ones. And to a proportionately lesser degree, even to the smaller out-of-the-blue earthquakes. Just saying. GoatGuy
Let’s see… aftershocks. Insofar as historic data has shown the Original Big One in almost all (P=0.97) of the cases contains substantially over 75{22800fc54956079738b58e74e4dcd846757aa319aad70fcf90c97a58f3119a12} of the total event released seismic energy. The aftershocks plural over the following weeks-to-years (but mostly months””) then dissipate an additional ⅓ more energy. 3 parts Big One”” 1 part all the Little Ones together. Now and again with a bit of a tongue-in-cheek: There’s a Big One. OK boys’n’girls what is going to follow? Aftershocks! Scary ones at various unsettling moments of the day and night in the weeks that follow! And even with this marvel-of-retrospective-AI”” the exact moment of the day or night remains a mystery. So the “”””being prepared for the next aftershock”””” is kind of mendaciously attributed to the AI team. I”””” a “”””regular I”””””” can also predict aftershocks. They’re agonna happen folks. After Big Ones. And to a proportionately lesser degree”” even to the smaller out-of-the-blue earthquakes. Just saying.GoatGuy”””””””
From a planning perspective though, getting a tighter prediction of the aftershock location and strength spread might be helpful when setting up command posts/distribution points/evac stations to guide disaster recovery ops. You can set up closer, within your statistical risk tolerance, without getting so close that an aftershock overly disrupts recovery ops.
Let’s see… aftershocks.
Insofar as historic data has shown, the Original Big One in almost all (P=0.97) of the cases contains substantially over 75% of the total event released seismic energy. The aftershocks, plural, over the following weeks-to-years (but mostly “months”) then dissipate an additional ⅓ more energy. 3 parts Big One, 1 part all the Little Ones together.
Now, and again, with a bit of a tongue-in-cheek: There’s a Big One. OK boys’n’girls, what is going to follow? Aftershocks! Scary ones, at various unsettling moments of the day and night in the weeks that follow! And even with this marvel-of-retrospective-AI, the exact moment of the day or night remains a mystery. So the “being prepared for the next aftershock” is kind of mendaciously attributed to the AI team.
I, a “regular I”, can also predict aftershocks. They’re agonna happen, folks. After Big Ones. And to a proportionately lesser degree, even to the smaller out-of-the-blue earthquakes.
Just saying.
GoatGuy