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Martarizal
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Electrical & Electronics Engineering Energy Physics Other

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Hybrid DAE-GAN Model with U-Net Architecture for Seismic Signal Denoising Eko Priyatno; Ahmad Kadarisman; Santoso Soekirno; Martarizal
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA08

Abstract

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.
Early Detection of Seismic Signal Anomalies Using Raspberry Pi 5 and Lightweight Machine Learning Models Ahmad Kadarisman; Imam Fachruddin; Santoso Soekirno; Hanif Andi Nugraha; Benyamin Heryanto Rusanto; Martarizal
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA14

Abstract

Data integrity is crucial for seismic monitoring systems, but is often compromised by anthropogenic or instrumental anomalies. This paper proposes a lightweight edge computing framework using Raspberry Pi 5 for real-time anomaly detection. MiniSEED data from the high-noise TOJI station were processed through segmentation, statistical or spectral feature extraction, and unsupervised models (isolation forest and autoencoder). The results show a detection latency of 78-113 ms with minimal resource consumption (<35% CPU, <200 MB RAM) and 82% correlation with ground-truth anomalies. This framework can be used on networked seismographs with limited resources such as those of the BMKG.
Co-Authors Ahmad Kadarisman Benyamin Heryanto Rusanto Eko Priyatno Hanif Andi Nugraha Imam Fachruddin Santoso Soekirno