  The recent explosion in the availability of echosounder data from diverse
ocean platforms has created unprecedented opportunities to observe the marine
ecosystems at broad scales. However, the critical lack of methods capable of
automatically discovering and summarizing prominent spatio-temporal echogram
structures has limited the effective and wider use of these rich datasets. To
address this challenge, we develop a data-driven methodology based on matrix
decomposition that builds compact representation of long-term echosounder time
series using intrinsic features in the data. In a two-stage approach, we first
remove noisy outliers from the data by Principal Component Pursuit, then employ
a temporally smooth Nonnegative Matrix Factorization to automatically discover
a small number of distinct daily echogram patterns, whose time-varying linear
combination (activation) reconstructs the dominant echogram structures. This
low-rank representation provides biological information that is more tractable
and interpretable than the original data, and is suitable for visualization and
systematic analysis with other ocean variables. Unlike existing methods that
rely on fixed, handcrafted rules, our unsupervised machine learning approach is
well-suited for extracting information from data collected from unfamiliar or
rapidly changing ecosystems. This work forms the basis for constructing robust
time series analytics for large-scale, acoustics-based biological observation
in the ocean.
