  Source separation involves the ill-posed problem of retrieving a set of
source signals that have been observed through a mixing operator. Solving this
problem requires prior knowledge, which is commonly incorporated by imposing
regularity conditions on the source signals, or implicitly learned through
supervised or unsupervised methods from existing data. While data-driven
methods have shown great promise in source separation, they often require large
amounts of data, which rarely exists in planetary space missions. To address
this challenge, we propose an unsupervised source separation scheme for domains
with limited data access that involves solving an optimization problem in the
wavelet scattering covariance representation space$\unicode{x2014}$an
interpretable, low-dimensional representation of stationary processes. We
present a real-data example in which we remove transient, thermally-induced
microtilts$\unicode{x2014}$known as glitches$\unicode{x2014}$from data recorded
by a seismometer during NASA's InSight mission on Mars. Thanks to the wavelet
scattering covariances' ability to capture non-Gaussian properties of
stochastic processes, we are able to separate glitches using only a few
glitch-free data snippets.

Machine Learning;Earth and Planetary Astrophysics;Signal Processing