Integrating Measurement Uncertainty for Enhanced Reliability in Digital Bathymetric Models

Background: Nowadays mapping of underwater topography has been greatly facilitated by high-resolution systems such as multibeam echosounders (MBES). Nevertheless, the extensive data collected by these instruments are often contaminated with varying degrees of errors. This calls for caution in constructing Digital Bathymetric Models (DBMs), particularly for critical applications like waterway navigation.

Objective: This master thesis aims to explore and enhance the quality of DBMs by incorporating measurement uncertainties systematically into the modeling process. By addressing these uncertainties, the thesis seeks to increase the reliability and precision of DBMs, thereby improving their applicability in critical areas

Specific Methodology:

1. Systematic Addressing of Measurement Uncertainties: The thesis will focus on developing a methodology to systematically handle measurement uncertainties. This will involve analyzing the types and sources of errors in MBES data and devising strategies to mitigate their impact on the DBM.
2. Development and Validation of Enhanced DBMs: The thesis will culminate in the creation of enhanced DBMs that integrate these uncertainties. The effectiveness of these models will be validated through a series of tests and comparisons with traditional DBMs.
3. Provided Material provided:
   1. Files containing MBES data point clouds.
   2. Software tools for uncertainty quantification and modeling software.

Programming Requirement:

Proficiency in Python and/or MATLAB is crucial. These programming languages will be used extensively for data analysis, uncertainty management, and model development.