Investigating the Vertical Uncertainty in MBES Measurements

• Published in: 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
• Main Author: Tengku Ali T.A.; Abbas M.A.; Mustafar M.A.; Said M.S.M.; Hashim N.M.; Sulaiman S.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132776463&doi=10.1109%2fCSPA55076.2022.9781857&partnerID=40&md5=36993fe5f44039023a5b59bc7ca0b373

Obtaining the information of morphology and sediment composition using Multi-Beam Echo-Sounder Systems (MBES) is becoming the standard practice in the marine community. The leading information used for achieving that information is bathymetry, backscatter strength measurements, and its derivatives. However, like any other measurement system, the depth and its derivatives are contaminated by the uncertainties inherent in MBES and its auxiliary used sensors, survey configuration and environment. Therefore, using any bathymetry derivatives without accounting for these uncertainties might hamper the result of acoustic seafloor classification. In this contribution, uncertainties from the real MBES observations are estimated and compared with the available models of MBES vertical uncertainty from the literature. The uncertainties were calculated from the different water depths of 8m, 20m and 29m. Generally, there are some discrepancies between the predicted model and the measurements, but the differences are relatively small and within the order of magnitude of the predictions. The echo-sounder contributes the most to the total vertical uncertainty. Further investigation, such as accounting for slopes when measuring the MBES uncertainties, should be imposed to reduce the standard deviation, and the results could reflect that the uncertainties left were contributed from the MBES only. This indicates the importance of the MBES uncertainties knowledge when the bathymetric features want to use for sediment classifications. © 2022 IEEE.