smousavi05/Unsupervised_Deep_Learning
Unsupervised (Self-Supervised) Clustering of Seismic Signals Using Deep Convolutional Autoencoders
This project helps seismologists or earthquake early warning system operators automatically categorize seismic signals. It takes raw seismic waveform data as input and groups similar signals together, helping to distinguish different types of seismic events or noise. The output provides clustered seismic data, useful for enhancing earthquake early warning systems.
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Use this if you need to automatically sort large volumes of unlabeled seismic data, such as discriminating between local and distant earthquakes or identifying different first-motion polarities.
Not ideal if you already have perfectly labeled seismic datasets and prefer a supervised classification method.
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Apr 07, 2022
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