A quality factor of forecasting error for sounding data in MBES

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, it requires processing large amount of data. A preprocessing method that performs in real time, automatically identifies the outliers in multi-beam bathymetry data, and provides corresponding bathymetry estimates, is able to provide a lot of effective information for the post-processing for improving the data processing efficiency and ensuring data quality. In this work, we propose quality factor of forecasting error (QE) for detecting the outliers and forecasting the depth in multi-beam bathymetry data. On the basis of the existing quality factor model for seabed detection method, and under the assumption of smooth seafloor terrain, we use the quality factor (QF) to select a suitable seabed detection method and eliminate the sounding points which correspond to poor echo characteristics. The uncertainty inferred by the quality factor is used as the initial parameter of 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 second fitting and Kalman filtering, the depth of each point is predicted. The quality factor 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 a good detection capability for impulse anomalies, cluster anomalies, and seabed topography undulations. In addition, the proposed method gives smooth depth prediction values. The simulation results and analysis show that the proposed algorithm further detects the outliers in depth sequence on the basis of QF and forecasts the sounding point in real time. The QE threshold based on the relative depth is easy to select and is suitable for different sounding system. It provides effective outlier detection information for post-processing.

A Dual Robust Strategy for Removing Outliers in Multi-Beam Sounding to Improve Seabed Terrain Quality Estimation

“…Experiments have found that substituting aberrant sounding points into polynomial fitting functions will significantly contaminate the overall model and result in mistakes in the parameters that are derived from the solution [ 15 ]. Furthermore, polynomial functions are not capable to accurately represent the real terrain indefinitely [ 12 ]. This model is typically appropriate for identifying locations with notable anomalies and slight variations in the topography.…”

“…In addition, Santos et al employed wavelet transform (WT) to improve the quality of nearshore water depth estimation [ 11 ]. A quality factor forecasting error (QFQE) was presented by Zhou et al to identify outliers and forecast depth in multi-beam sounding data [ 12 ]. QFQE approach found an outlier collection of sound points by fitting and estimating each sounding point using sliding windows and Kalman filtering.…”

“…Waveform filtering WMA [9]; GF [10]; WT [11]; Kalman filtering [12]; etc. In units of Ping signal; Relatively low efficiency; Outliers of significant features.…”