Abstract

Abstract. High-resolution bathymetry forms critical datasets for marine geoscientists. It can be used to characterize the seafloor and its marine habitats, to understand past sedimentary records, and even to support the development of offshore engineering projects. Most methods to acquire bathymetry data are costly and can only be practically deployed in relatively small areas. It is therefore critical to develop cost-effective and advanced techniques to produce regional-scale bathymetry datasets. This paper presents an integrated workflow that builds on satellites images and 3D seismic surveys, integrated with historical depth soundings, to generate regional high-resolution digital elevation models (DEMs). The method was applied to the southern half of Australia's North West Shelf and led to the creation of new high-resolution bathymetry grids, with a resolution of 10 × 10 m in nearshore areas and 30 × 30 m elsewhere. The vertical and spatial accuracy of the datasets have been assessed using open-source Laser Airborne Depth Sounder (LADS) and multibeam echosounder (MBES) surveys as a reference. The comparison of the datasets indicates that the seismic-derived bathymetry has a vertical accuracy better than 1 m + 2 % of the absolute water depth, while the satellite-derived bathymetry has a depth accuracy better than 1 m + 5 % of the absolute water depth. This 30 × 30 m dataset constitutes a significant improvement of the pre-existing regional 250 × 250 m grid and will support the onset of research projects on coastal morphologies, marine habitats, archaeology, and sedimentology. All source datasets are publicly available, and the methods are fully integrated into Python scripts, making them readily applicable elsewhere in Australia and around the world. The regional digital elevation model and the underlying datasets can be accessed at https://doi.org/10.26186/144600 (Lebrec et al., 2021).

Highlights

  • The North West Shelf (NWS) is a ∼ 2400 km long carbonate platform along the northern margin of Australia, stretching between 10 and 25◦ S (James et al, 2004)

  • Unlike other datasets that are reduced to mean sea level (MSL), navigation charts are referenced with respect to the lowest astronomical tide (LAT)

  • The 30 × 30 m bathymetry grid resulting from the compilation of the reflection-derived bathymetry and navigation-derived bathymetry was compared with the multibeam echosounder (MBES) bathymetry grids available on the NWS to assess its vertical accuracy (Fig. 6b, c, d)

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Summary

Introduction

The North West Shelf (NWS) is a ∼ 2400 km long carbonate platform along the northern margin of Australia, stretching between 10 and 25◦ S (James et al, 2004). But they almost solely build on sparse seabed samples (Carrigy and Fairbridge, 1954; Jones and Australian Government Publishing Services, 1973; Dix, 1989; James et al, 2004; Baker et al, 2008) This observation can be explained by the limited coverage of open-source high-resolution geophysical datasets (Fig. 2), which in turn can be explained by the prohibitive cost of acquiring such datasets. The integration of lowresolution and multi-source datasets can allow the interpolation of high-resolution grids and help improve the extent of mapped areas Based on this approach, high-resolution bathymetry compilations were created over the Sahul Shelf, the Northern Territory, and the Great Barrier Reef using MBES data, airborne lidar bathymetry (ALB) surveys, satellite-derived bathymetry (SDB) data, and singlebeam echosounder surveys (Beaman, 2017, 2018). Quality check processes and discussions on the vertical and positional accuracy are presented for each dataset included in the compilation

Processing tools
Australian Bathymetry and Topography Grid
SRTM-derived digital elevation model
Multibeam echosounder bathymetry
National Intertidal Digital Elevation Model
ENC navigation chart
Open-source LADS airborne lidar bathymetry
Overview
Data processing
Data limitation
Data source
Seismic-derived bathymetry compilation
Data calibration
Data accuracy
Satellite data
Calibration points
Pre-processing
Derivation of the initial bathymetry
Correction of the initial bathymetry
Generation of the bathymetry stack
Post-processing data cleaning
Merging strategy
Findings
Summary
Full Text
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