Optimizing an inner-continental shelf geologic framework investigation through data repurposing and machine learning

Geosciences
By: , and 

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Abstract

The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5,400 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 groundtruth 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 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.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Optimizing an inner-continental shelf geologic framework investigation through data repurposing and machine learning
Series title Geosciences
DOI 10.3390/geosciences9050231
Volume 9
Issue 6
Year Published 2019
Language English
Publisher MDPI
Contributing office(s) Woods Hole Coastal and Marine Science Center
Description 24 p.
Country United States
State Delaware, Maryland, Virginia