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Earthquake magnitude estimation using a two-step convolutional neural network.

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Titel: Earthquake magnitude estimation using a two-step convolutional neural network.
Autoren: Liu, Xinliang, Ren, Tao, Chen, Hongfeng, Dimirovski, Georgi M., Meng, Fanchun, Wang, Pengyu
Quelle: Journal of Seismology; Feb2025, Vol. 29 Issue 1, p241-256, 16p
Abstract: In this paper, an efficient two-step convolutional neural network (CNN) procedure is proposed to estimate earthquake magnitude using raw waveform data up to only 4 s after the P wave onset. In the proposed procedure, magnitude estimation is split into classification task and regression task. The classification task trains a CNN model to estimate the magnitude range by employing unsure responses that represent the classification decision boundary. In addition, the regression task trains two CNN models to estimate the specific magnitudes of large and small earthquakes, respectively. After training, the classification model achieves an accuracy of 98.63%. The mean absolute error (MAE) of the large earthquake regression and the small earthquake regression models are 0.26 and 0.46, respectively. The ideology behind the two-step procedure effectively address two main issues in earthquake early warning (EEW) systems: reducing missed alert caused by seismometer saturation and improving the accuracy of estimating specific magnitudes. Currently, this procedure has been connected to China Earthquake Networks Center (CENC) for real-time monitoring. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Seismology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Earthquake magnitude estimation using a two-step convolutional neural network.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Xinliang%22">Liu, Xinliang</searchLink><br /><searchLink fieldCode="AR" term="%22Ren%2C+Tao%22">Ren, Tao</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Hongfeng%22">Chen, Hongfeng</searchLink><br /><searchLink fieldCode="AR" term="%22Dimirovski%2C+Georgi+M%2E%22">Dimirovski, Georgi M.</searchLink><br /><searchLink fieldCode="AR" term="%22Meng%2C+Fanchun%22">Meng, Fanchun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Pengyu%22">Wang, Pengyu</searchLink>
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  Data: Journal of Seismology; Feb2025, Vol. 29 Issue 1, p241-256, 16p
– Name: Abstract
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  Group: Ab
  Data: In this paper, an efficient two-step convolutional neural network (CNN) procedure is proposed to estimate earthquake magnitude using raw waveform data up to only 4 s after the P wave onset. In the proposed procedure, magnitude estimation is split into classification task and regression task. The classification task trains a CNN model to estimate the magnitude range by employing unsure responses that represent the classification decision boundary. In addition, the regression task trains two CNN models to estimate the specific magnitudes of large and small earthquakes, respectively. After training, the classification model achieves an accuracy of 98.63%. The mean absolute error (MAE) of the large earthquake regression and the small earthquake regression models are 0.26 and 0.46, respectively. The ideology behind the two-step procedure effectively address two main issues in earthquake early warning (EEW) systems: reducing missed alert caused by seismometer saturation and improving the accuracy of estimating specific magnitudes. Currently, this procedure has been connected to China Earthquake Networks Center (CENC) for real-time monitoring. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Seismology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10950-024-10258-9
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            NameFull: Liu, Xinliang
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              M: 02
              Text: Feb2025
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              Y: 2025
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