Hybrid DAE-GAN Model with U-Net Architecture for Seismic Signal Denoising - Eko Priyatno; Ahmad Kadarisman; Santoso Soekirno; Martarizal (Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI, 2026)
Seismic data is important for geophysical studies, but it often faces interferences that complicate the analysis of underground structures. This research introduces a new method using deep learning to reduce noise in seismic recordings. It combines a Denoising Autoencoder (DAE) with a Generative Adversarial Network (GAN). In this method, a U-Net model serves as the Generator to create a noise-free signal from the contaminated input. A CNN-based Discriminator distinguishes between the generated and original signals. The Generator's loss function includes Mean Squared Error (MSE) for accuracy and Adversarial Loss for realistic features. The model was trained on the STEAD dataset and its performance evaluated with measures like Signal-to-Noise Ratio (SNR), RMSE, and PRD. Results show that this model improves SNR and produces a clean signal similar to the original both visually and spectrally. This approach could enhance automation and efficiency in preprocessing seismic data.