(A) Maximum Likelihood Classification (MLC) with two inputs, slope, and ISO-classified backscatter for the whole survey area. (B) MLC with five inputs, slope, ISO-classified backscatter, bathymetry, hillshaded-relief, and curvature.
Optimizing an Inner-Continental Shelf Geologic Framework Investigation
through Data Repurposing and Machine Learning
through Data Repurposing and Machine Learning
By Woods Hole Coastal and Marine Science Center
May 29, 2019
USGS and NOAA collaborate to create high-resolution maps on the Atlantic continental shelf between Delaware and Virginia.
The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected sea floor mapping 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 new high-resolution, broad-scale geologic maps of the seafloor using machine learning. These new 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. Read the Geosciences article, Optimizing an Inner-Continental Shelf Geologic Framework Investigation through Data Repurposing and Machine Learning
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