Mr. Wei Liu Profile

Wei Liu

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Proceedings Article | 4 April 2024 Paper

A classification model for signals of seismic events based on vision transformer

Ruijia Ji, Yongming Huang, Wei Liu

Proceedings Volume 13078, 130780C (2024) https://doi.org/10.1117/12.3024685

KEYWORDS: Transformers, Earthquakes, Fourier transforms, Signal processing, Time series analysis, Artificial neural networks, Neural networks

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Accurate seismic event classifications offer crucial information to earthquake alarms, and precise classifications in time can effectively reduce casualties and losses in seismic events. This paper proposes a classification model using a Vision Transformer network trained and tested using 8667 time-series records intercepted from seismic observatories. Different forms of input, including Gramian angular field and short-time Fourier transform, are used as the image input, and the regular time-series input is also tested. The precisions of classifications of explosions, collapses, and natural earthquakes are 93.12%, 92.49%, and 92.63%, respectively. The overall accuracy of classification is 92.74%, with short-time frequency maps of multiple channels of seismic signals as input, showing the ability of the proposed network model to extract and identify features of different types of seismic events.

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