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DeepShake uses machine learning to quickly estimate the intensity of an earthquake’s vibration
A deep spatiotemporal neural network trained on more than 36,000 earthquakes offers a new way to quickly predict the intensity of Earth’s shaking once an earthquake strikes, researchers report at the 2021 SSA annual meeting.
The DeepShake system analyzes seismic signals in real time and issues an advanced strong vibration warning based on the characteristics of the early waves detected from the earthquake.
DeepShake was developed by Daniel J. Wu, Avoy Datta, Weiqiang Zhu, and William Ellsworth at Stanford University.
The earthquake data used to train the DeepShake network came from seismic recordings of the Ridgecrest, California 2019 sequence. When its developers tested DeepShake’s potential using the actual vibration of the Ridgecrest magnitude 7.1 earthquake on July 5, the neural network sent simulated alerts between 7 and 13 seconds before the ground shaking arrived HD to locations in the Ridgecrest area.
The authors emphasized the novelty of using deep learning for rapid early warning and direct prediction from seismic records alone. “DeepShake is able to capture signals in seismic waveforms across space and time dimensions,” Datta explained.
He added that DeepShake demonstrates the potential of machine learning models to improve the speed and accuracy of earthquake warning systems.
“DeepShake aims to improve earthquake early warnings by making vibration estimates directly from ground motion observations, eliminating some of the intermediate steps used by traditional warning systems,” Wu said.
Wu explained that many early warning systems first determine the location and magnitude of the earthquake, and then calculate the ground motion of the site based on the equations for predicting the ground movement.
He added, “Each of these steps could lead to an error that could weaken the expectations of the earth’s shaking.”
To address this, the DeepShake team turned to a neural network approach. The series of algorithms that make up a neural network are trained without the researcher identifying the “important” signals for the network to use in its predictions. The network learns the features that optimally predict the strength of the future vibration directly from the data.
“We have noticed by building other neural networks for use in seismology that they can learn all sorts of interesting things, and thus they may not need the epicenter and size of the earthquake to predict well,” Wu said. “DeepShake is being trained on a pre-defined network of seismic stations, so that the local characteristics of those seismic stations become part of the training data.”
“When training a machine learning model from start to finish, we really believe that these models are able to utilize this additional information to improve accuracy,” he said.
Wu, Datta, and colleagues view DeepShake as a complement to operational ShakeAlert in California, adding to its earthquake early warning systems toolkit. Data added, “We are really excited to expand DeepShake beyond Ridgecrest, and enhance our work for the real world, including failures such as broken terminals and high network latency.”
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