Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

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Deep Learning for Geophysics: Current and Future Trends (2021, background): “The sparsity assumption presumes that the data are sparse under certain transforms (Donoho & Johnstone, 1995), such as the curvelet domain (Herrmann & Henn-enfent, 2008) or other time-frequency domains (Mousavi, Langston, et al., 2016; Mousavi & Langston 2016, 2017).”
Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, methodology): “In this approach, noisy time series are first transformed into the timefrequency domain using a time-frequency transform, such as a wavelet transform [22, 23, 24, 25, 26, 27], Short Time Fourier Transform (STFT) [28], S-transform [29], curvelet transform [30, 31, 32], dreamlet transform [33], contou”
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019, methodology): “The de-signaling algorithm used here is a combination of the methods introduced in [24] and [25] that identiﬁes the anomalous spectral features associated with earthquake signals (based on statistical considerations) in a continuous wavelet domain.”