End-to-end seismic signals denoising via deep residual convolution and self-attention mechanisms - ZHAO Botao; KANG Zuheng; HE Yayun; PENG Junqing; ZHANG Xulong; QU Xiaoyang; TAN Yipei; CHEN Yule; XIAO Chunguang; WANG Jianzong (大数据, 2025)
Denoising of seismic waveform signals is crucial for seismic monitoring and seismological research. To this end, we propose an end-to-end deep learning method for denoising seismic waveforms. The method combines the deep convolutional network with the multi-head self-attention mechanism. We employ a residual encoder-decoder structure, which is particularly well-suited for processing signals with complex backgrounds and rich details. At the same time, the multi-head self-attention mechanism can capture long-range dependencies. By jointly constraining the model with a consistent correlation loss and a frequency-domain mean squared error loss, outstanding denoising performance is achieved in both the time and frequency domains. Evaluation of the publicly available dataset STEAD shows that our method outperforms traditional and existing deep learning methods in two key metrics: peak signal-to-noise ratio (PSNR) and signal correlation coefficient (CC), achieving a Pearson correlation of 0.918 and a PSNR of 36.79, reaching the state-of-the-art performance. Furthermore, we have further validated our method using the seismic waveform data recorded by the Beijing-Tianjin-Hebei earthquake early warning network, which started operating in 2021, and the results indicate that our approach can effectively suppress noise while better preserving seismic signals (achieving a CC of 0.70 and a PSNR of 35.26).