Abstract
The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5400 km2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for different objectives, the comprehensive coverage and variety of data (bathymetry, backscatter, imagery and physical samples) presents an opportunity to merge collections and create high-resolution, broad-scale geologic maps of the seafloor. This compilation of data repurposes hydrographic data, expands the area of geologic investigation, highlights the versatility of mapping data, and creates new geologic products that would not have been independently possible. The data are classified using a variety of machine learning algorithms, including unsupervised and supervised methods. Four unique classes were targeted for classification, and source data include bathymetry, backscatter, slope, curvature, and shaded-relief. A random forest classifier used on all five source data layers was found to be the most accurate method for these data. Geomorphologic and sediment texture maps are derived from the classified acoustic data using over 200 ground truth samples. The geologic data products can be used to identify sediment sources, inform resource management, link seafloor environments to sediment texture, improve our understanding of the seafloor structure and sediment pathways, and demonstrate how ocean mapping resources can be useful beyond their original intent to maximize the footprint and scientific impact of a study.
Highlights
The U.S Geological Survey (USGS) began as a collaborative project with the University of Delaware, the National Park Service, the Mid-Atlantic Coastal Resilience Institute, and the NationalOceanographic and Atmospheric Administration (NOAA) in 2014 to define the geologic framework of the Delmarva Peninsula coastal system
Since the hydrographic data were not acquired with geologic products as an objective and, as such, contained numerous artifacts associated with acquisition that typically pose a challenge to machine learning, the first goal of this study was to determine if the repurposed hydrographic data could be used to conduct an automated seafloor classification, given the artifacts and acquisition differences among surveys
The iso cluster (ISO) classification performed with the lowest total accuracy due to its its inability to distinguish all four target classes (Table 3), ISO performed at 100% when inability to distinguish all four target classes (Table 3), ISO performed at 100% when distinguishing between high and low backscatter areas
Summary
The U.S Geological Survey (USGS) began as a collaborative project with the University of Delaware, the National Park Service, the Mid-Atlantic Coastal Resilience Institute, and the National. Oceanographic and Atmospheric Administration (NOAA) in 2014 to define the geologic framework of the Delmarva Peninsula coastal system. This mapping effort builds on recent and ongoing hydrographic, geologic, and ecological studies in the area and represents an opportunity to construct a geospatial framework around existing datasets, and acquire new data to fill knowledge gaps. NOAA carried out 31 hydrographic surveys between 2006 and 2013 at 40-m line spacing using multibeam echosounders and sidescan sonars, over more than 5000 square kilometers of the mid-Atlantic inner-continental shelf adjacent to the Delmarva Peninsula, in water depths of 2.5 to.
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