Data DOI: 10.26022/IEDA/329906
Citation:
Restreppo, Giancarlo, et al., (2021), Machine-learning predicted logarithmic values of marine mass accumulation rates on the seafloor. Marine Geoscience Data System (MGDS). doi:10.26022/IEDA/329906
Title:
Machine-learning predicted logarithmic values of marine mass accumulation rates on the seafloor
Abstract:
This data set consists of log10 values of machine-learning-based mass accumulation rates and linear uncertainty on the seafloor, globally. The Geospatial Predictive Seafloor Model (GPSM) of the United States Naval Research Laboratory was trained on real-world observations from 43 peer-reviewed sources (n = 1744) to predict marine mass accumulation rates on a 5-arc-minute map using a k-nearest neighbor algorithm. Original (non-log space values) are in g/cm2/yr. The model and results are described in Restreppo et al. (2021). The data files are in netCDF (.nc) grid format. The file called Log10_prediction_5m.nc contains the predicted sediment accumulation rate in log10(g/cm2/yr). The file called Linear_uncertainty_5m.nc is the uncertainty, in g/cm2/yr.
Creator(s):
Restreppo, Giancarlo
Wood, Warren
Graw, Jordan
Phrampus, Benjamin
Date Available:
2021-06-08
Date Created:
2020-11-16
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/