Regional seismic discrimination research at LLNL

Walter, William R.; Mayeda, Kevin; Goldstein, Peter; Patton, Howard J.; Jarpe, S.; Glenn, L. A.

doi:10.2172/124999

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Extensive research on regional earthquake event identification has shown that the spectral amplitude ratio of P-waves and S-waves, known as P/S ratio, is an effective method for distinguishing earthquakes from underground nuclear explosions [1][2][3] . Subsequently, the development of Magnitude and Distance Amplitude Correction (MADC) techniques has been used to refine the P/S discriminant [4][5][6] . The research on nuclear explosion identification provides various criteria that can potentially be used for distinguishing natural earthquakes from artificial blasting events.…”

Section: Introductionmentioning

confidence: 99%

Research on the seismic signal classification algorithm based on SE-DenseNet

Wang,

Huang,

Ding

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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Addressing the issue of seismic signal classification and recognition for natural earthquakes and artificial blasting, a deep learning model suitable for seismic signal classification and recognition is proposed. In this study, a dataset is created by selecting 14,001 natural earthquake waveforms and 12,870 artificial blasting waveforms from the seismic waveform data provided by the China National Earthquake Science Data Sharing Centre for the years 2010-2016. Based on the DenseNet architecture, an attention mechanism is introduced into the original Dense Block, adaptively enhancing valuable feature channels, thereby improving the model's perception and expression capabilities for important information. The model achieves an accuracy of 96.25% on the test set, surpassing the compared methods. Experimental results demonstrate that the attention-enhanced DenseNet exhibits excellent classification effectiveness and generalization ability when applied to seismic signals.

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