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August 11, 2025Open Access

Machine Learning-based Seismic Signal Denoising with a Case Study of the 2006 Yogyakarta Earthquake Aftershock Sequence

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Authors

RPRosadi PutraTarumanagara UniversityIMI MadrinovellaMRMohamad Lutfi Ramadhan

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Implication

This analysis reveals machine learning enhances seismic signal quality, improving detection of P-waves and S-waves.

Key Points

  • Deep Denoiser improves seismic signal clarity, enhancing noise separation and overall analysis.
  • Results showed higher signal clarity with a significant increase in the detection of P-waves and S-waves.
  • Observational analysis focused on the 2006 Yogyakarta aftershock sequence, characterized by low Signal-to-Noise Ratio.
  • Machine learning techniques may advance how seismic data is interpreted, aiding future earthquake hazard assessments.

Cite This Study

Putra et al. (2025) studied this question.

synapsesocial.com/papers/68a35eeb0a429f7973327f88https://doi.org/10.1088/1755-1315/1521/1/012020
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