Year: 2023
Type: article
Abstract: The identification of unnatural earthquake events is one of the tasks of earthquake rapid report. The identification accuracy is of great significance for improving the quality of earthquake catalogue and seismological research. In this work, a seven-layer convolution neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was used as input to obtain the waveform image classifier, and then the time-frequency spectrum of explosion and collapse was used as input to obtain the time-frequency spectrum classifier. The two classifiers were used to identify earthquake, explosion and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as explosion events by the waveform image classifier were reidentified by the time-frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, explosion and collapse is 97.50%, 95.87% and 86.84% respectively, with an average identification accuracy of 96.13%. The experimental results show that the two-step convolution neural network can extract the characteristics of seismic signals from different angles, and get a good result in seismic event classification.
Source: Journal of Seismology
Institution: Missouri University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G5699196316
OpenAlex ID: W4380050171