Offshore seismic exploration requires accurate positions of towed streamers and seismic receivers. Because the actual positions of underwater streamers cannot be accurately determined, few effective evaluation methods for streamer positioning algorithms/software have been developed, besides simulations. However, most simulated data used in streamer positioning research are static; they lack physical meaning and neglect to consider the complex motion characteristics of the streamers caused by the towing vessel and current. To address this limitation, we have developed a C++ program for simulating streamer positioning observations, called SimStr. Based on the motion equation of underwater streamers, we use the finite-difference method to obtain a dynamic streamer shape, for which a box method is used to discretize the streamer motion in space and time. The positioning data are then simulated based on the dynamic streamer shape. We elaborate the structure of the program, for which the code of core algorithms was implemented to have good extendibility, efficiency, and portability, while ensuring high performance. As an example, we determine the simulated streamers of a vessel turning around to demonstrate the effectiveness of the code. Furthermore, we input the simulated data into a self-developed streamer positioning program and a commercial software to evaluate their positioning results and ultimately validate the simulated data.

Towed streamer positioning is a critical step in marine seismic survey, and the positioning accuracy of streamers directly affects the quality and reliability of seismic imaging. However, when existing streamer positioning algorithms are applied to modern multi-streamer survey, the positioning accuracy can be limited by imperfections in the positioning model. Besides, a comprehensive evaluation and comparison for streamer positioning algorithms is not established yet due to the lack of benchmark. In this article, we propose two analytical positioning models in the least-squares (LS) framework to fusing compass and acoustic sensors in a tightly-coupled form for multi-streamer positioning, and for the first time, the performance of different streamer positioning methods and models is fully evaluated and compared using both simulation and real-world data. First, the polynomial curve streamer positioning model is improved by cubic splines for segmented fitting, which is a modeling on coordinates (MOC) model. Second, a curve integration model for multi-streamer positioning is proposed, which is a modeling on shape (MOS) model. Furthermore, these two proposed positioning models, along with the polynomial curve model and numerical method, are evaluated and compared based on the six-streamer simulation data and ten-streamer real-world data. The results demonstrate better robustness and adaptability of the proposed positioning models, and compared to the MOC model, the MOS model achieves better positioning performance with fewer parameters.

Towed streamer positioning is a vital and essential stage in marine seismic exploration, and accurate hydrophone coordinates exert a direct and significant influence on the quality and reliability of seismic imaging. Current methods predominantly rely on analytical polynomial models for towed streamer positioning; however, these models often produce significant errors when fitting to streamers with high curvature, particularly during turning scenarios. To address this limitation, this study introduces a novel multi-streamer analytical positioning method that uses a hybrid harmonic function to model the three-dimensional coordinates of streamers. This approach mitigates the substantial modeling errors associated with polynomial models in high-curvature conditions and better captures the dynamic characteristics of streamer fluctuations. Firstly, the mathematical model for the hybrid harmonic function is constructed. Then, the algorithmic implementation of the model is detailed, along with the derivation of the error equation and the multi-sensor fusion solution process. Finally, the validity of the model is verified using both simulated and field data. The results demonstrate that, in the turning scenario without added error, the proposed harmonic model improves simulation accuracy by 35.5% compared to the analytical polynomial model, and by 27.2% when error is introduced. For field data, accuracy improves by 18.1%, underscoring the model’s effectiveness in significantly reducing errors associated with polynomial models in turning scenarios. The performance of the harmonic function model is generally comparable to that of the polynomial model in straight scenarios.

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.

In ocean bottom node (OBN) seismic exploration, the precise positioning of OBNs directly affects seismic data quality. However, complex marine environments often introduce intricate outliers into collected acoustic positioning data, which severely restricts the positioning accuracy and stability of OBNs. To address issues such as poor threshold adaptability and low continuous outlier detection capability in existing outlier detection methods when processing OBN acoustic observation data, this paper proposes a quality control method for seabed acoustic positioning based on an improved Random Sample Consensus (RANSAC) method. This method employs a dynamic threshold that adapts to the observation fitting value and inlier rate, and introduces time-series uniform grouping sampling, thereby optimizing threshold setting and sampling strategy to enhance outlier detection performance and computational efficiency. Simulation results demonstrate that compared to the conventional RANSAC, the improved method exhibits superior outlier detection performance and computational efficiency, while achieving optimal positioning accuracy. Field experiment results demonstrate that the improved method can effectively detect and eliminate both large and small outliers, as well as continuous outliers. Compared to the fixed-threshold method, the improved RANSAC method improves positioning accuracy by 28.8% and 42.2% in the Direction Alongline (DA) and Direction Crossline (DC), respectively. Additionally, it achieves a 13.3% improvement in DA positioning accuracy and a 49.0% increase in computational efficiency over the conventional RANSAC method. The research findings demonstrate that the improved RANSAC method effectively enhances the accuracy and efficiency of OBN positioning, providing technical support for high-precision positioning in complex marine seismic exploration.