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Toward earthquake early warning: A convolutional neural network for rapid earthquake magnitude estimation

Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude, i.e., large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category. However, the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance. Therefore, in this study, Deep Learning (DL) algorithms are introduced to assist with EEW. For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center (CENC), this paper proposes a DL model (EEWMagNet), which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention. Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude. Moreover, the comparison experiments demonstrate that the epicenter distance information is indispensable, and the normalization has a negative effect on the model to capture accurate amplitude information.

Availability
309551Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
Artificial Intelligence in Geosciences
Call Number
551
Publisher
Beijing : KeAi Communications Co. Ltd..,
Collation
8 hlm PDF, 5.714 KB
Language
Inggris
ISBN/ISSN
2666-5441
Classification
551
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.4, December 2023
Subject(s)
Massive tectonic earthquakes
Earthquake early warning
Magnitude classification
DenseBlock
Multi-head attention
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

File Attachment
  • Toward earthquake early warning: A convolutional neural network for rapid earthquake magnitude estimation
    Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude, i.e., large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category. However, the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance. Therefore, in this study, Deep Learning (DL) algorithms are introduced to assist with EEW. For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center (CENC), this paper proposes a DL model (EEWMagNet), which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention. Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude. Moreover, the comparison experiments demonstrate that the epicenter distance information is indispensable, and the normalization has a negative effect on the model to capture accurate amplitude information.
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