An Improved RANSAC Method for Outlier Detection in OBN Acoustic Positioning

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.