Uncertainty assessment applied to marine subsurface datasets

Abstract

A recently released voxel model quantifying aggregate resources of the Belgian part of the North Sea includes lithological properties of all Quaternary sediments and modelling-related uncertainty. As the underlying borehole data come from various sources and cover a long time span, data-related uncertainties should be accounted for as well. Applying a tiered data-uncertainty assessment to a composite lithology dataset with uniform, standardised lithological descriptions and rigorously completed metadata fields, uncertainties were qualified and quantified for positioning, sampling and vintage. The uncertainty on horizontal positioning combines navigational errors, on-board and off-deck offsets, and underwater drift. Sampling-gear uncertainty evaluates the suitability of each instrument in terms of its efficiency of sediment yield per lithological class. Vintage uncertainty provides a likelihood of temporal change since the moment of sampling, using the mobility of fine-scale bedforms as an indicator. For each uncertainty component, quality flags from 1 (very uncertain) to 5 (very certain) were defined, and converted into corresponding uncertainty percentages meeting the input requirements of the voxel model. Obviously, an uncertainty-based data selection procedure, aimed at improving the confidence of data products, reduces data density. Whether or not this density reduction is detrimental to the spatial coverage of data products, will depend on their intended use. At the very least, demonstrable reductions in spatial coverage will help to highlight the need for future data acquisition and to optimise survey plans. By opening up our subsurface model with associated data uncertainties in a public decision support application, policy makers and other end users are better able to visualise overall confidence and identify areas with insufficient coverage meeting their needs. Having to work with a borehole dataset that is increasingly limited with depth below the seabed, engineering geologists and geospatial analysts in particular will profit from a better visualisation of data-related uncertainty.Thematic collection: This article is part of the Mapping the Geology and Topography of the European Seas (EMODnet) collection available at: https://www.lyellcollection.org/cc/EMODnet

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

“…This method can be limited to indicate the presence or absence of data or be very complex producing a full range of quantitative error ranges (e.g., Bardossy and Fodor, 2001). Kint et al (2020) presented an approach to assess data uncertainty for a well dataset in the Quaternary succession of the Belgian Continental Shelf. They produced confidence maps based on datasets from different origins and time periods.…”

“…Generalizing the approach proposed by Kint et al (2020) with application to arbitrary spatial data and using the Inverse Distance Weight (IDW) for our analysis, we implemented, in a preliminary way, a method to weight the accuracy/confidence of geological surfaces. We quantified for each geological horizon: i) the data density contributing to assess the lateral accuracy and the depth variability, ii) the accuracy based on the data density and spatial auto-correlation converted into a probability (Inverse Distance Weight -IDW) describing the confidence on the data at each point of the study area and iii) the error associated with the depth of the geological surfaces due to discrepancies between the data of different origins where different guesses exist.…”

Adaptive And Reliable GPS Uncertain Position Estimation an Insightful Oceanography and Geography Applications

“…As the depth of the borehole dataset decreases, engineering geologists and geospatial analysts will benefit most from a more accurate depiction of the uncertainty that comes along with that depth. [3] McCammon, S [2021] Ocean monitoring is costly and time-consuming, but autonomous robots can help. In this study, demonstrate a system for autonomously identifying and monitoring ocean fronts using a team of ASVs and UUVs (AUVs).…”

“…Earthquake and volcanic, detection and prediction are emerging research areas where the geo-dynamical phenomena and their source mechanisms have a larger impact on the tectonic plate movement [2]. GPS devices are also used to mark the contours between local ground motion and sea level where the variations are dependent on the spatiotemporal scales of the electronic position estimators [3]. One of the most crucial applications is oceanic en-route, where the position estimation is relative and its accuracy depends on the spatiotemporal effects of the instruments and the physical systems.…”

Modeling the Cross‐Sectional Dynamics of Tidal Sandbanks in Sediment‐Scarce Conditions

“…Field observations have identified several sandbanks where the bed between banks is not covered by sand. For example, the Flemish Banks and Zeeland Banks expose a layer of hard clay in the troughs (Hademenos et al., 2019; Kint et al., 2021), and the bed between the Norfolk Banks consists of gravel (Caston, 1972). In order to understand sandbank dynamics, let us review the literature on sandbank modeling.…”