The shape and position information of towed streamers is crucial for both implementing marine seismic exploration operations and analyzing exploration data. Streamer positioning accuracy directly impacts the quality and reliability of seismic imaging. Existing polynomial curve models exhibit deviations between the calculated and actual shapes during streamer turning. This paper proposes a segmented fitting positioning model based on spline curves. It is mathematically rigorous and applicable to complex scenarios. First, the specific function expression of the spline curve model is constructed. Then, using a cubic spline as an example, the segmented fitting method is explained, incorporating smoothness constraints at the connection points. The error equations for positioning observations and the calculation processes for curve parameters and hydrophone coordinates are derived. Finally, the model is verified through simulations and field tests. The experimental results show that, compared with the polynomial curve model, the spline curve model improves positioning accuracy by 47.1% in simulations involving six streamers and by 20.0% and 35.0% in field tests with six and ten streamers, respectively. In straight scenarios, both models perform similarly. Thus, the spline model can effectively reduce the modeling errors of the polynomial curve model under high-curvature conditions.

Accurate hydrophone positioning is essential for the quality of marine seismic imaging and the reliability of structural interpretation. To mitigate the accuracy degradation of the conventional polynomial model under unevenly distributed or missing acoustic observations, we propose a hydrophone positioning method that incorporates subnetwork node constraints. An iterated extended Kalman filter (IEKF) estimates the acoustic node coordinates and their full within-subnetwork posterior covariances, which serve as weighted virtual observations in a global polynomial adjustment. In a straight-line simulation, the mean front/rear node deviations decrease from 1.83/1.25 m with the polynomial model to 0.28/0.47 m, and the mean hydrophone deviation decreases by 19.0%/24.7% relative to the polynomial and hybrid harmonic function models. Field datasets A/B show lower node discrepancies and mean hydrophone discrepancy reductions of 18.8%/22.9% versus the polynomial model and 16.5%/18.1% versus the hybrid harmonic function model, relative to commercial references. These percentages are specific to the sequences examined. Paired statistics that account for temporal dependence support positive mean reductions across all tested block lengths. The method maintains low deviations under the tested persistent observation loss, with computation times comparable to those of the polynomial model. These results indicate lower simulated deviations and closer agreement with commercial references under the evaluated conditions.