A Novel Approach to Estimate Three-Dimensional Current Fields Using Coastal Acoustic Tomography System in the Yeosu Bay, South Korea

Coastal acoustic tomography (CAT) is a technology used to monitor changes in current or water temperature fields by analyzing reciprocal acoustic travel time signals between CAT stations in coastal seas. We have developed a novel method for estimating three-dimensional current fields by combining an inverse method with an artificial intelligence (AI) model. To test the newly-developed method, we first ran the ray tracing model ‘Bellhop’ to compute reciprocal travel times among five CAT stations using high-resolution ocean model outputs. These five stations correspond to the locations where in-situ CAT systems were established for continuous monitoring of current changes in the Yeosu Bay, South Korea. Subsequently, the range-averaged currents at the five layers were estimated by incorporating the travel time difference data into an AI model trained with the same ocean model outputs. Finally, the inverse method was applied to each layer to estimate the 3-D current fields. Validation results revealed that this method performed reasonably well, producing time-varying two-layer-like current fields in the study area. They occasionally revealed an out-of-phase relationship between the upper and lower layers depending on the tidal phases. This method yielded average RMSEs of less than 5 cm/s on six simulation paths. These results indicate that our method can be effectively applied to 3-D current field monitoring in coastal seas using a CAT system.