Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits
A multi-stage ensembled machine learning model was developed to estimate metal abundances in seafloor massive sulfide deposits worldwide. The modeling framework integrates (1) KMeans++ clustering to identify geochemical groupings based on enrichment controls, (2) Random Forest classification to assign geochemical labels to vent fields with incomplete or absent geochemical data, and (3) XGBoost regression to generate high-fidelity predictions of metal concentrations. This USGS model application data release includes all scripts, input files, and output files necessary to apply the model to estimate concentrations of cobalt, gold, and zinc. This model is not limited by spatial boundaries and is intended for application to any oceanic location with appropriate input data.
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "PCMSC Science Data Coordinator", "@type": "vcard:Contact", "hasEmail": "mailto:pcmsc_data@usgs.gov" } |
| description | A multi-stage ensembled machine learning model was developed to estimate metal abundances in seafloor massive sulfide deposits worldwide. The modeling framework integrates (1) KMeans++ clustering to identify geochemical groupings based on enrichment controls, (2) Random Forest classification to assign geochemical labels to vent fields with incomplete or absent geochemical data, and (3) XGBoost regression to generate high-fidelity predictions of metal concentrations. This USGS model application data release includes all scripts, input files, and output files necessary to apply the model to estimate concentrations of cobalt, gold, and zinc. This model is not limited by spatial boundaries and is intended for application to any oceanic location with appropriate input data. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P13PYBJL", "mediaType": "application/http", "description": "Landing page for access to the data" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.67f010a4d4be02766d636810.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_67f010a4d4be02766d636810 |
| keyword |
[ "CMHRP", "Coastal and Marine Hazards and Resources Program", "PCMSC", "Pacific Coastal and Marine Science Center", "U.S. Geological Survey", "USGS", "USGS:67f010a4d4be02766d636810", "critical minerals", "economy", "geoscientificInformation", "machine learning", "marine chemistry", "mineral resources", "modeling", "oceans", "sea-floor characteristics" ] |
| modified | 2025-12-16T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -180, -90, -180, 90 |
| theme |
[ "geospatial" ] |
| title | Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits |
