Data DOI: 10.26022/IEDA/329769
Citation:
Restreppo, Giancarlo, et al., (2020), Machine-learning predicted logarithmic values of marine benthic vertical sediment accumulation rate on the seafloor. Marine Geoscience Data System (MGDS). doi:10.26022/IEDA/329769
Title:
Machine-learning predicted logarithmic values of marine benthic vertical sediment accumulation rate on the seafloor
Abstract:
This data set consists of logarithmic values of machine-learning-based vertical sediment accumulation rates along the seafloor, globally. The Geospatial Predictive Seafloor Model (GPSM) of the United States Naval Research Laboratory was trained on real-world observations from 89 peer-reviewed sources (n = 1031) to predict marine accumulation rates on a 5-arc-minute map using a k-nearest neighbor algorithm. Original (non-log space values) are in cm/yr. The model and results are described in Restreppo et al. (2020). The data files are in netCDF grid format. The file called prediction.nc contains the predicted sediment accumulation rate in cm/yr. File prediction_sdev.nc is the standard deviation.
Creator(s):
Restreppo, Giancarlo
Wood, Warren
Phrampus, Benjamin
Date Available:
2020-08-05
Date Created:
2020-08-05
Data Type(s):
Interpretation:Geologic:SedimentaryEnvironments
Resource Type:
Dataset
File Format(s):
application/x-netcdf
Data Curated by:
Version:
1
Language:
en
License:
Creative Commons Attribution-NonCommercial-Share Alike 3.0 United States [CC BY-NC-SA 3.0] URI: http://creativecommons.org/licenses/by-nc-sa/3.0/us/
Related Information
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