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Journal: |
Journal of Geophysical Research 2019 No.1
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Title:
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Author:
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Men‐Andrin Meier1 , Zachary E. Ross1 , Anshul Ramachandran2 , Ashwin Balakrishna2 , Suraj Nair2 , Peter Kundzicz2 , Zefeng Li1 , Jennifer Andrews1 , Egill Hauksson1 , and Yisong Yue2 |
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Adress: |
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Abstract:
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In earthquake early warning (EEW), every sufficiently impulsive signal is potentially the first
evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere
nuisance signals. One of the most fundamental—and difficult—tasks in EEW is to rapidly and reliably
discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on
very little information, typically a few seconds worth of seismic waveforms from a small number of stations.
As a result, current EEW systems struggle to avoid discrimination errors and suffer from false and missed
alerts. In this study we show how modern machine learning classifiers can strongly improve real‐time
signal/noise discrimination. We develop and compare a series of nonlinear classifiers with variable
architecture depths, including fully connected, convolutional and recurrent neural networks, and a model
that combines a generative adversarial network with a random forest. We train all classifiers on the same
data set, which includes 374 k local earthquake records (M3.0–9.1) and 946 k impulsive noise signals. We
find that all classifiers outperform existing simple linear classifiers and that complex models trained directly
on the raw signals yield the greatest degree of improvement. Using 3‐s‐long waveform snippets, the
convolutional neural network and the generative adversarial network with a random forest classifiers both
reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications
stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results
suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts. |
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