Martian time-series unraveled: A multi-scale nested approach with
factorial variational autoencoders
- URL: http://arxiv.org/abs/2305.16189v3
- Date: Tue, 20 Feb 2024 04:18:18 GMT
- Title: Martian time-series unraveled: A multi-scale nested approach with
factorial variational autoencoders
- Authors: Ali Siahkoohi and Rudy Morel and Randall Balestriero and Erwan Allys
and Gr\'egory Sainton and Taichi Kawamura and Maarten V. de Hoop
- Abstract summary: Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator.
This problem is inherently ill-posed and is challenged by the variety of timescales exhibited by sources.
We propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering spectra.
- Score: 13.758556734097374
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unsupervised source separation involves unraveling an unknown set of source
signals recorded through a mixing operator, with limited prior knowledge about
the sources, and only access to a dataset of signal mixtures. This problem is
inherently ill-posed and is further challenged by the variety of timescales
exhibited by sources. Existing methods typically rely on a preselected window
size that determines their operating timescale, limiting their capacity to
handle multi-scale sources. To address this issue, we propose an unsupervised
multi-scale clustering and source separation framework by leveraging wavelet
scattering spectra that provide a low-dimensional representation of stochastic
processes, capable of distinguishing between different non-Gaussian stochastic
processes. Nested within this representation space, we develop a factorial
Gaussian-mixture variational autoencoder that is trained to (1)
probabilistically cluster sources at different timescales and (2) independently
sample scattering spectra representations associated with each cluster. As the
final stage, using samples from each cluster as prior information, we formulate
source separation as an optimization problem in the wavelet scattering spectra
representation space, aiming to separate sources in the time domain. When
applied to the entire seismic dataset recorded during the NASA InSight mission
on Mars, containing sources varying greatly in timescale, our multi-scale
nested approach proves to be a powerful tool for disentangling such different
sources, e.g., minute-long transient one-sided pulses (known as ``glitches'')
and structured ambient noises resulting from atmospheric activities that
typically last for tens of minutes. These results provide an opportunity to
conduct further investigations into the isolated sources related to
atmospheric-surface interactions, thermal relaxations, and other complex
phenomena.
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