Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice
Shelf, Antarctica
Abstract
Advances in machine learning (ML) techniques and computational capacity
have yielded state-of-the-art methodologies for processing, sorting, and
analyzing large seismic data sets. In this work, we consider an
application of ML for automatically identifying dominant types of
impulsive seismicity contained in observations from a 34-station
broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica
from 2014 to 2017. The RIS seismic data contain signals and noise
generated by many glaciological processes that are useful for monitoring
the integrity and dynamics of ice shelves. Deep clustering was employed
to efficiently investigate these signals. Deep clustering automatically
groups signals into hypothetical classes without the need for manual
labeling, allowing for comparison of their signal characteristics and
spatial and temporal distribution with potential source mechanisms. The
method uses spectrograms as input and encodes their salient features
into a lower-dimensional latent representation using an autoencoder, a
type of deep neural network. For comparison, two clustering methods are
applied to the latent data: a Gaussian mixture model (GMM) and deep
embedded clustering (DEC). Eight classes of dominant seismic signals
were identified and compared with environmental data such as
temperature, wind speed, tides, and sea ice concentration. The greatest
seismicity levels occurred at the RIS front during the 2016 El Niño
summer, and near grounding zones near the front throughout the
deployment. We demonstrate the spatial and temporal association of
certain classes of seismicity with seasonal changes at the RIS front,
and with tidally driven seismicity at Roosevelt Island.