Developing Operational Tsunami Detection with DAS: Bridging Theory and Observation

Although observations of tsunami waves using Distributed Acoustic Sensing (DAS) remain relatively scarce, examples do exist, confirming the feasibility of direct detection of tsunami-induced signals on submarine cables (e.g., Xiao et al., 2024; Tonegawa & Araki, 2024, 2026). These observations, however, also highlight practical limitations including low signal-to-noise ratios at long periods, challenges in discriminating tsunami signals from environmental and oceanographic background noise, directional sensitivity of the recorded strain field, and complexities introduced by bathymetry and seafloor coupling. These studies emphasize that while direct detection is feasible, robust and reliable operational deployment requires further refinement in both instrumentation and signal processing.

Beyond observational evidence, an analytical framework has been developed to quantify the coupling between tsunami-induced pressure fields and the strain recorded by submarine cables. Becerril et al. (2026) demonstrate that hydrostatic pressure perturbations associated with tsunami waves induce measurable horizontal strain through both, the effects of cable elasticity (via Poisson’s response) and seafloor compliance, with additional contributions from shear stresses induced by horizontal fluid motion in shallow depths. This formulation provides a quantitative basis for interpreting DAS observations and assessing expected signal amplitudes relative to instrumental noise levels.

Taken together, these analytical and observational advances underscore the potential of DAS as an ancillary sensor for next-generation Tsunami Early-Warning Systems (TEWS). Addressing the identified technical challenges is therefore a prerequisite for operational adoption. In this context, current efforts will be outlined focused on integrating these insights into the development of improved DAS system designs, including enhanced low-frequency sensitivity, optimized deployment strategies, and application-specific processing methodologies, with the objective of enabling reliable, real-time tsunami detection in future operational settings.