Seismic Forecasting and Event Classification

The field of seismic research is moving towards leveraging deep learning techniques to improve forecasting and event classification. Recent studies have demonstrated the potential of transformer-based models and machine learning approaches to capture realistic ground motion patterns and differentiate between tectonic and anthropogenic events. Noteworthy papers include:

  • A study introducing a transformer-based model for forecasting seismic waveforms, which shows promise for data-driven seismic forecasting.
  • A paper applying machine learning methods to classify seismic events in a region with significant mining-induced activity, achieving high accuracy rates. These developments highlight the importance of innovative methodologies in advancing the field of seismic research.

Sources

Forecasting Seismic Waveforms: A Deep Learning Approach for Einstein Telescope

Machine learning approaches to seismic event classification in the Ostrava region

On the Shape of Latent Variables in a Denoising VAE-MoG: A Posterior Sampling-Based Study

Reducci\'on de ruido por medio de autoencoders: caso de estudio con la se\~nal GW150914

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