A machine learning approach for underwater gas leakage detection
This addresses a critical safety and environmental monitoring issue for underwater CCS projects, but it is incremental as it applies existing machine learning methods to a new domain.
The paper tackles the problem of early detection of gas leaks in underwater carbon capture and storage facilities using passive acoustic monitoring and machine learning, achieving good performance in detection based on classification algorithms with simulated data from the Brazilian shore.
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.