基于WaveNet和全波形的地震反方位角智能估计模型

肖卓; 莫金卫; 张皓哲; 黄华娟; 张莹莹; 徐敏

doi:10.3799/dqkx.2025.184

Volume 51 Issue 1

Jan. 2026

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Xiao Zhuo, Mo Jinwei, Zhang Haozhe, Huang Huajuan, Zhang Yingying, Xu Min, 2026. Seismic Back-Azimuth Estimation Model Based on WaveNet and Full Waveform Data. Earth Science, 51(1): 90-103. doi: 10.3799/dqkx.2025.184

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Xiao Zhuo, Mo Jinwei, Zhang Haozhe, Huang Huajuan, Zhang Yingying, Xu Min, 2026. Seismic Back-Azimuth Estimation Model Based on WaveNet and Full Waveform Data. Earth Science, 51(1): 90-103. doi: 10.3799/dqkx.2025.184

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Seismic Back-Azimuth Estimation Model Based on WaveNet and Full Waveform Data

doi: 10.3799/dqkx.2025.184

1.
College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
2.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
3.
China Earthquake Networks Center, China Earthquake Administration, Beijing 100045, China

Received Date: 2025-05-01
Publish Date: 2026-01-25

Abstract

Abstract

Earthquake location is fundamental to both early warning systems and studies of the Earth's deep structure, yet its accuracy remains challenging to be improved. Using three-component waveform data from the China National Seismic Network, this study develops a single-station back-azimuth estimation method based on deep learning. We compare the performance of a standard convolutional neural network with that of a WaveNet architecture under three input settings: P-wave only, surface-wave only, and full-waveform input. Results show that WaveNet combined with full-waveform input performs best, benefiting from dilated convolutions and residual connections that enhance its ability to extract long-range temporal features. The model achieves an average back-azimuth deviation of only 0.04°, with a coefficient of determination (R²) of 0.99. Independent tests demonstrate strong generalization capability, with the mean absolute deviation and variance reduced by 58.70% and 28.21%, respectively, compared with the traditional surface-wave polarization method. The findings indicate that deep learning with full-waveform input can substantially improve single-station location accuracy, offering effective technical support for earthquake early warning and seismic monitoring in challenging environments.
- earthquake location,
- back-azimuth,
- WaveNet,
- deep learning,
- full waveform,
- seismology

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References(43)

References

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Seismic Back-Azimuth Estimation Model Based on WaveNet and Full Waveform Data

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