A quality factor of forecasting error for sounding data in MBES

Abstract

Abstract The multi-beam sounding system achieves ultra-wide coverage and high-resolution measurement. Its significantly increased data density has great advantages in accurately depicting the topography of the seabed. However, this requires processing large amounts of data. A preprocessing method that performs in real time automatically identifies outliers in multi-beam bathymetry data, and provides corresponding bathymetry estimates, is able to provide a lot of effective information for the post-processing to improve the data processing efficiency and ensure data quality. In this work, we propose a quality factor (QF) forecasting error (QE) for detecting outliers and forecasting depth in multi-beam bathymetry data. On the basis of the existing QF model for a seabed detection method, and under the assumption of smooth seafloor terrain, we use the QF to select a suitable seabed detection method and eliminate the sounding points that correspond to poor echo characteristics. The uncertainty inferred by the QF is used as the initial parameter for Kalman filter estimation and the depth-value prediction model is formulated. The sounding sequence is sorted by the median value by using the sliding window method. After a second fitting and Kalman filtering, the depth of each point is predicted. A QF model based on forecasting errors is adopted to simplify and unify the outlier detection standards. The selection rules of window length and detection threshold are deeply studied on the basis of simulations performed in this work. For appropriate parameters, the proposed algorithm shows good detection capability for impulse anomalies, cluster anomalies and seabed topography undulations. In addition, the proposed method gives smooth depth-prediction values. Simulation results and analysis show that the proposed algorithm further detects outliers in depth sequences on the basis of the QF and forecasts the sounding points in real time. The QE threshold based on the relative depth is easy to select and is suitable for different sounding systems. This provides effective outlier detection information for post-processing.