Exploring the 2019 Ridgecrest seismic data with unsupervised deep learning 

We analyze the seismic data continuously recorded in the vicinity of the Mw7.4 2019 Ridgecrest earthquake with an unsupervised deep learning method proposed by Seydoux et al. (2020), in search of seismic signatures of physical signatures of the earthquake preparation phase. We downloaded data from the 3 different stations B918, with a 100 Hz sampling frequency, SRT and CLC with a 40 Hz sampling frequency. Using a scattering network combined with an independent component analysis, we define stable waveform features and cluster the continuous signals extracted from a sliding window before proposing cluster-based interpretations of the seismic signals. We also further discuss our results with external datasets such as independently-obtained seismicity catalogs in the area. We also investigate a manifold-learning-based representation (UMAP) of the data in 2D from the scattering network. According to our first results merged with a catalog analysis we are able to separate various events from the noise and identify several types of seismicity and noises.