Seismic waveform denoising method using a CBAM-equipped U-Net with SNR and CC incorporated into the loss function

Abstract

We propose a deep-learning-based denoising model optimized for Japanese seismic waveform data. Unlike the conventional DeepDenoiser [1], our model uses MeSO-net observations, which contain strong anthropogenic and urban noise, and adopts a U-Net architecture with a Convolutional Block Attention Module (CBAM) [4] for enhanced feature extraction. The input consists of a two-channel spectrogram representing the real and imaginary parts of the complex STFT. The target is an amplitude ratio mask derived from the magnitude spectra of noisy and clean signals. The loss function combines mean squared error (MSE) with signal-to-noise ratio (SNR) and cross-correlation (CC) terms to preserve waveform similarity. The model converged after 27 epochs and achieved an evaluation score of 299.71, far exceeding DeepDenoiser (15.19). Average SNRs reached ~170, and CC values exceeded 0.9 across all components. These results demonstrate that incorporating SNR and CC terms improves denoising performance while maintaining signal fidelity for Japanese seismic data.