  Underwater gas reservoirs are used in many situations. In particular, Carbon
Capture and Storage (CCS) facilities that are currently being developed intend
to store greenhouse gases inside geological formations in the deep sea. In
these formations, however, the gas might percolate, leaking back to the water
and eventually to the atmosphere. The early detection of such leaks is
therefore tantamount to any underwater CCS project. In this work, we propose to
use Passive Acoustic Monitoring (PAM) and a machine learning approach to design
efficient detectors that can signal the presence of a leakage. We use data
obtained from simulation experiments off the Brazilian shore, and show that the
detection based on classification algorithms achieve good performance. We also
propose a smoothing strategy based on Hidden Markov Models in order to
incorporate previous knowledge about the probabilities of leakage occurrences.

Machine Learning;Machine Learning