ERROR BUDGET ESTIMATION ON MULTIBEAM DEPTH MEASUREMENT IN SHALLOW WATER

• Main Author: Daniella
• Format: Final Project
• Language: Indonesia
• Subjects: Geografi matematis
• Online Access: https://digilib.itb.ac.id/gdl/view/81151
• Institution: Institut Teknologi Bandung

This research is aiming at estimating the error budget of depth reading from a Multibeam Echo Sounding (MBES) in shallow water and calculating the amount of data in the whole depth and in each depth which can be estimated. This research is done by field survey in Seribu Archipelago near to Island; Pramuka, Panggang, and Karya. Depth data were collected from MBES and Single Beam Echo Sounder (SBES). Research is held by compare the reading from MBES to the SBES. The SBES reading is calibrated by bar check technique, it is assumed that SBES data is the true value. The result of the comparison between MBES and SBES is be known as Total Error Multibeam (TEM). This research is focused in systematic error, especially from instrument, sound speed, vessel motion, vessel draft, and vessel squat and settlement. This errors form a pattern between SBES and MBES reading. Error budget is being estimated using the standard from National Oceanic and Atmospheric Administration (NOAA) (2014). Estimation is done by calculating the percentage of each errors. For calculating the amount of data which can be calculated is also use those standard. If TEM value is smaller or equal to the standard, TEM is dominated by those five error resources so it can be estimated. But, if it is bigger than the standard, TEM still contain another systematic errors and blunder so it cannot be estimated. As the result, the error budget for each error resources is respectively is 28.09% from instrument, sound speed, and vessel motion, 8.43% from vessel draft, and 7.30% from vessel squat and settlement. Over all, the depth data which can be estimated is about 13.67%. In each depth, the depth data which can be estimated is classified into 5 meters range of depth. In 5-10 meter of depth there are 9.92% data which can be estimated, 10.36% in 10-15 m, 23.79% in 15-20 m, 8.71% in 20-25 m, 16.76% in 25-30 m, and 0% in 30-40 m.