Spatio-temporal characterisation of underwater noise through semantic trajectories

TL;DR

The paper addresses the problem of mapping underwater noise pollution from maritime activity, focusing on fishing vessels in the Northern Adriatic Sea. It introduces a spatio-temporal framework that reconstructs AIS trajectories, enriches them with semantic attributes, and stores them in MobilityDB to compute noise maps via the relation $RL = SL - TL$ across frequencies. Key contributions include a refined propagation model that combines spherical and mode-stripping transmission loss, speed- and activity-dependent source levels, and a 1 km × 1 km grid with 60 s sampling, demonstrated on 2020 AIS data and calibrated with SOUNDSCAPE measurements. The results illustrate how fishing activity drives detectable noise patterns, offer qualitative validation, and provide a scalable tool for policymakers and ecologists to assess underwater noise risks even where hydrophones are limited.

Abstract

Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by vessels is an important challenge for the preservation of the marine environment. In this paper we propose a model for the spatio-temporal characterisation of the underwater noise generated by vessels. The approach is based on the reconstruction of the vessels' trajectories from Automatic Identification System (AIS) data and on their deployment in a spatio-temporal database. Trajectories are enriched with semantic information like the acoustic characteristics of the vessels' engines or the activity performed by the vessels. We define a model for underwater noise propagation and use the trajectories' information to infer how noise propagates in the area of interest. We develop our approach for the case study of the fishery activities in the Northern Adriatic sea, an area of the Mediterranean sea which is well known to be highly exploited. We implement our approach using MobilityDB, an open source geospatial trajectory data management and analysis platform, which offers spatio-temporal operators and indexes improving the efficiency of our system. We use this platform to conduct various analyses of the underwater noise generated in the Northern Adriatic Sea, aiming at estimating the impact of fishing activities on underwater noise pollution and at demonstrating the flexibility and expressiveness of our approach.

Figures (9)

• Figure 1: Representation of the three temporal types activity, speed and trip.
• Figure 2: Main steps in the calculation of the noise maps.
• Figure 3: Underwater noise bivariate maps for June 2020. The red stars are the hydrophones of SOUNDSCAPE.
• Figure 4: During, and post-Covid underwater noise at 125 Hz for the months of April, and June 2020.
• Figure 5: Number of AIS data per day of the week in June 2020.