Multi-scale clustering and source separation of InSight mission seismic data
This work addresses the challenge of analyzing seismic data with varied timescales for planetary science missions, offering a method to isolate sources like glitches and atmospheric noises, which is incremental as it builds on existing unsupervised techniques but adapts them for multi-scale scenarios.
The paper tackles the problem of unsupervised source separation for multi-scale time series data from planetary missions, proposing a framework that uses wavelet scattering spectra and a factorial variational autoencoder to cluster and separate sources at different timescales, successfully applying it to the NASA InSight seismic dataset on Mars to disentangle sources like minute-long glitches and tens-of-minute ambient noises.
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 in time series data from planetary space missions. As such, a systematic multi-scale unsupervised approach is needed to identify and separate sources at different timescales. 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 variational autoencoder that is trained to probabilistically cluster sources at different timescales. To perform source separation, we use samples from clusters at multiple timescales obtained via the factorial variational autoencoder as prior information and formulate an optimization problem in the wavelet scattering spectra representation space. When applied to the entire seismic dataset recorded during the NASA InSight mission on Mars, containing sources varying greatly in timescale, our approach disentangles 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, and provides an opportunity to conduct further investigations into the isolated sources.