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Comparative Evaluation of Deep Learning-Based Seismic Signal Denoising on an Edge Computing Platform 1 Department of Physics Faculty of Mathematics and Natural Sciences, Universitas Indonesia Abstract Urban earthquake monitoring is critically hindered by anthropogenic noise that spectrally overlaps with seismic signals, rendering conventional filtering methods insufficient. This study evaluates two deep-learning-based denoising methods, UrbanDenoiser and U-Net to identify the most effective approach for cleaning noisy seismic signals from two distinct stations in Indonesia while enabling on-site implementation on a low-power edge computing platform. UrbanDenoiser was trained on data from station TNGI and U-Net on data from station BKJI, after which both models were cross-evaluated on each other^s station data to assess generalization capability across different noise environments. Both models were compiled into Hailo Executable Format (HEF) and deployed on a Raspberry Pi 5 equipped with the Hailo-8L AI accelerator (13 TOPS) for real-time inference. Evaluation of UrbanDenoiser across 60,473 inference windows from station TNGI yielded a Pearson correlation of r = 0.9759 and an SNR of 6.89 dB, with a 5.00% anomaly rate at the 95th-percentile threshold, indicating strong denoising performance across low-to-moderate urban noise conditions (input SNR range: 3.7-11.4 dB). Initial results demonstrate that the Raspberry Pi 5 + Hailo-8L platform is capable of running real-time seismic denoising inference, validating its feasibility as an on-site solution for urban seismic monitoring networks in Indonesia. Keywords: Seismic denoising, UrbanDenoiser, U-Net, Raspberry Pi 5, Hailo-8L, cross-station evaluation Topic: Instrumentation and Computational Physics |
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