Elias Adediran
Ph.D. Dissertation Proposal Defense
Ocean Engineering
Wednesday, June 10, 2026, 11:00am
Chase 105
Abstract
Quantifying uncertainty in interpolated bathymetry remains a key challenge with direct implications for navigation, science, and industry. Fully surveying the seafloor at the required resolution is often costly and inefficient, and navigational products have historically relied on sparse, systematic sampling of the seafloor combined with interpolation to preserve spatial continuity. Although modern multibeam systems have significantly improved coverage efficiency, interpolation remains necessary to fill data gaps, particularly in set-line spacing and skunk-striping surveys where unsampled regions remain common and interpolation uncertainty is rarely formally quantified.
This dissertation proposes a unified framework for the estimation, integration, and application of interpolation uncertainty within hydrographic workflows. It is hypothesized that interpolation errors can be modeled as spatially structured stochastic processes and characterized using nearby observations under the assumption that seafloor statistical properties exhibit locally consistent behavior over short spatial scales. The first chapter will investigate spectral and spatial statistical methods for quantifying interpolation uncertainty in systematically sampled surveys, with the goal of developing an algorithm capable of producing interpolation uncertainty estimates that reliably bound interpolation error across diverse seafloor morphologies.
The second chapter will investigate how interpolation uncertainty can be integrated with modeled total vertical uncertainty (TVU), including slope-induced effects, to support hydrographic quality classification within interpolated regions. The third chapter will examine how integrated interpolation-aware uncertainty estimates can support near real-time adaptive hydrographic survey design by informing line spacing decisions and survey configurations required to satisfy prescribed grid uncertainty targets while balancing survey efficiency and data quality requirements.
This research will contribute a unified operational framework for modeling interpolation uncertainty in bathymetry and propagating its effects through hydrographic quality assessment and adaptive survey design workflows. The proposed framework is expected to improve the reliability of bathymetric products by explicitly incorporating interpolation uncertainty into hydrographic quality assessment and survey planning, while supporting more efficient survey design that balances acquisition cost, survey efficiency, and required data quality standards.
Bio
Elias Adediran is a doctoral researcher in Ocean Engineering at the University of New Hampshire’s Center for Coastal and Ocean Mapping – Joint Hydrographic Center, where his research focuses on improving digital bathymetric models by characterizing interpolation uncertainties. He holds a Master’s in Ocean Engineering: Ocean Mapping and graduate certificates in Ocean Mapping and Geospatial Science from UNH. Before that, he earned a first-class Bachelor of Science in Surveying and Geoinformatics from the University of Lagos and a National Diploma with distinction from Yaba College of Technology, Nigeria.
