J. Eur. Opt. Society-Rapid Publ. 2026, 22, 13 Ó The Author( s), published by EDP Sciences, 2026 https:// doi. org / 10.1051 / jeos / 2026008 Available online at: https:// jeos. edpsciences. org
EOSAM 2025 Guest editors: Omar El Gawhary, Stefan Witte, Ignacio Moreno
Journal of the European Optical Society-Rapid Publications
SHORT COMMUNICATION
Modeling of surface vessels using distributed acoustic sensing data and physics-based optimization
Pedro Martins 1, 2,*
, Ilmer Van Golde 3, Susana Silva 1, 2, Orlando Frazão 1, 2, and Ricardo Sousa 1, 2
1 INESC TEC – Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal 2 Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre s / n, 4169-007 Porto, Portugal 3 Instituto Hidrográfico, Rua das Trinas 49, 1249-093 Lisboa, Portugal
Received 15 December 2025 / Accepted 25 January 2026
Abstract. Technological advances in global communications depend significantly on robust and efficient longdistance infrastructures. One notable example is the submarine cable network. Installed on the ocean floor, these cables use fiber optic technology to transmit large volumes of data at high speed and low latency between continents. Beyond their primary communication function, recent innovations allow these cables to serve as Distributed Acoustic Sensing( DAS) systems, effectively converting tens of kilometers of passive fiber into massive, coherent arrays of vibration sensors. The primary objective of this project is to enhance maritime surveillance capabilities by integrating DAS technology with advanced kinematic modeling. This paper establishes a rigorous physical and mathematical framework for interpreting the acoustic signatures of surface vessels detected by bottom-mounted fibers. We derive the complete opto-acoustic transfer function, formulate the hyperbolic moveout equations based on a moving point-source solution to the wave equation, and implement a stochastic inversion scheme using Differential Evolution. By optimizing a correlation-based loss function, we demonstrate the ability to recover vessel trajectory, speed, and depth from complex interferometric patterns with speed estimation errors consistently below 1 %. This approach allows for the extraction of quantitative physical parameters from raw optical data, bridging the gap between photonics and hydroacoustics.
Keywords: Distributed acoustic sensing, Maritime surveillance, /-OTDR, Hyperbolic moveout, Differential evolution, Optimization.
1 Introduction
Distributed Acoustic Sensing( DAS) has revolutionized the field of optical monitoring by enabling the conversion of standard fiber optic cables into large-scale, ultra-sensitive vibration sensor arrays [ 1, 2 ]. Originally developed for vertical seismic profiling and pipeline monitoring within the oil and gas industry, DAS technology is now finding critical applications in environmental monitoring and critical infrastructure security. Specifically, submarine telecommunication cables can be repurposed as distributed seismic or acoustic sensors, providing meter-scale spatial resolution and continuous coverage over tens of kilometers of the ocean floor [ 3, 4 ]. Recent studies have demonstrated that DAS is capable of detecting underwater hydroacoustic signals, including the mechanical noise generated by surface vessels, with performance metrics comparable to conventional hydrophone arrays, although the Signal-to-Noise Ratio( SNR) may vary across frequency bands [ 5 ].
* Corresponding author: pedro. l. martins @ inesctec. pt
The primary challenge in transforming a DAS system into a reliable vessel tracking platform lies in the complexity of the signal processing required. As a ship moves through the water, it generates continuous acoustic noise resulting from engine vibrations and propeller cavitation. Because the ship moves relative to the stationary fiber on the seabed, these acoustic wavefronts reach different sections of the fiber at different times, producing distinct, geometry-dependent patterns in the recorded data. This paper presents a comprehensive mathematical formulation of these hyperbolic moveout patterns and introduces an inverse problem framework designed to extract precise vessel movement and physical characteristics directly from raw DAS waterfall plots.
Unlike prior studies, which have primarily focused on the simple detection or qualitative visualization of vessel signatures, our approach enables the quantitative inference of vessel dynamics and physical parameters from DAS measurements. Global maritime security currently relies heavily on Automatic Identification System( AIS) transponders [ 6 ]. However, AIS data is inherently vulnerable to spoofing, can
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