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地震预警系统使用AI预测摇动

Highway workers repair a hole that opened in the road as a result of the July 5, 2019 earthquake in Ridgecrest, California, about 150 miles (241 kilometers) north of Los Angeles.
Highway workers repair a hole that opened in the road as a result of the July 5, 2019 earthquake in Ridgecrest, California, about 150 miles (241 kilometers) north of Los Angeles. (Image credit: Robyn Beck/AFP via Getty Images)

An earthquake early warning system that usesartificial intelligence(AI) to predict how the ground will move during a temblor can give several seconds' advance notice that the shaking is coming.

在美国西海岸上已经存在了一种使用更传统的计算能力的类似系统。它被称为Shakealert,它通过检测地震运动的第一波来说,称为P波 - 然后计算导致大部分摇动的波浪的一组波 - 将到达。

The new system in development is called DeepShake, and it is also intended to provide a few seconds' warning of imminent shaking once an earthquake has started. However, DeepShake uses a deep neural network, a type of AI learning, to identify patterns from past地震为了预测如何从新的地震震动将如何旅行。这可能导致在不同地震易发地区的加工速度更快和更容易普遍。

Related:This millennium’s most destructive earthquakes

"When we set out on this project our goal was to beat the ground motion prediction equations that are currently used" to program shake-alert systems, said Avoy Datta, a master's student in electrical engineering at Stanford University who was part of the team that developed DeepShake. "They tend to be very slow. You need numerical solvers, running on supercomputers, and they can take minutes and hours to process."

In contrast, "If we run 25 DeepShake models, it takes around 6.1 milliseconds on a single research GPU [graphics processing unit]. " Datta told Live Science. “This is going to be blazing fast.”

Predicting shaking

在2013年4月23日在美国的地震学会的虚拟会议上,Datta和他的同事斯坦福大学朱伊尔吴报道了它们在训练迪斯夏季后的成果,以预测加利福尼亚州Ridgecrest附近地震的地面运动。Ridgecrest在地震活跃的东加州剪力区,并在2019年,一系列地震震动了该地区。最大的,一个幅度为7.1地震,于7月5日击中。

Datta, Wu and their colleagues used this earthquake sequence to train DeepShake to predict ground shaking in the area. They started with a dataset of more than 36,000quakes that struck Ridgecrestfrom July to September 2019 (most were quite tiny). They fed 80% of the dataset into the deep neural network, saving 10% for tweaking the parameters of the network and a final 10% for testing whether the network's outcomes matched reality.

The researchers programmed the network to assign more weight to the larger earthquakes in the sequence, which were relatively few, so that it could perform better as an early warning system — after all, the largest quakes are the ones that people need warning about the most.

Giving warnings

Despite the fact that DeepShake was given no information about the earthquake's location or type, it was able to warn of shaking at other seismic stations in the network between 3 and 13 seconds before it happened, Wu told Live Science. This is similar to the amount of advanced notice with ShakeAlert. Wu and Datta don't view this other system as a competitor, however. Rather, they said, DeepShake technology could be used to complement ShakeAlert. The researchers hope to expand the testing to other faults and earthquake sequences.

在任何特定位置的地面摇动可能是棘手的预测。例如,Shakealert未能在2019年在RidGecrest序列中最大的Quake发出警告,因为预计摇动不会达到某些区域的“轻摇”的计划的阈值,这确实经历了轻抖动。自2019年以来,Shakealert的开发人员已经改变了它,以纳入那些经验教训。吴说,深受教育网络的优势在于,他们自动合并了该网站的怪癖,因为它们基于过去在该地点摇动的经历。与Shakealert不同,它使用具有内置的假设的更多普遍方程式,在其使用的每个地区都必须培训深奶。然而,这种培训将捕获传统方程可能不会的模式。

“深深学习真正蓬勃发展的地方是有很多数据和许多复杂模式来揭开的地方,”吴说。

Originally published on Live Science.