The deep-sea convergence zone (CZ) is a critical phenomenon for long-range underwater acoustic propagation. Accurate prediction of its distance, width, and gain is essential for enhancing sonar detection performance. However, conventional ray-tracing models, which assume vertically stratified sound speed profiles (SSPs), fail to account for horizontal sound speed gradients in the mixed layer, leading to significant prediction errors. To address this, we propose a novel ray-tracing model that incorporates horizontally inhomogeneous SSPs in the mixed layer. Our approach combines empirical orthogonal function (EOF) decomposition with the Del Grosso sound speed formula to construct a continuous 3D sound speed field. We further derive a modified ray equation including horizontal gradient terms and solve it using a fourth-order Runge–Kutta method. Simulation and experimental validation in the South China Sea demonstrate that our model reduces the prediction error for the first CZ distance by 2.26%, width by 2.66%, and gain deviation by 5.85% compared to the Bellhop model. These results confirm the effectiveness of our method in improving CZ parameter prediction accuracy.

The conventional Bellhop ray model is constrained by its assumption of vertical stratification in sound speed, which limits its ability to accurately account for the deflection of ray trajectories caused by horizontal gradients in sound speed within the mixed layer. This limitation leads to inaccuracies in predicting convergence zone characteristics. This study introduces an enhanced ray model that incorporates the horizontal inhomogeneity of sound speed in the mixed layer, thereby enabling precise tracking of ray trajectories and accurate prediction of convergence zone characteristics. Experimental results demonstrate that, in comparison to the traditional Bellhop model, the proposed improved ray model reduces the prediction error of the distance to the first convergence zone in deep-sea environments by 2.26%. The enhanced model significantly enhances the accuracy of convergence zone characteristic predictions and can be effectively applied to the estimation of underwater acoustic detection ranges and the evaluation of sonar detection performance.