Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits

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Frequently anticipated questions:

What does this data set describe?

Title:
Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits
Abstract:
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.
Supplemental_Information:
See SMS-MetalML_reference-list.pdf for details on all external sources used in this work. See the README.md files for additional information on the operating system and software versions used to develop this model, the directory structure, and files not listed here in the metadata. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  1. How might this data set be cited?
    Maria C. Figueroa Matias, 20251215, Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits: Data Release DOI:10.5066/P13PYBJL, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    Other_Citation_Details:
    Suggested Citation: Figueroa Matias, M.C., 2025, Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits: U.S. Geological Survey data release, https://doi.org/10.5066/P13PYBJL.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -180
    East_Bounding_Coordinate: -180
    North_Bounding_Coordinate: 90
    South_Bounding_Coordinate: -90
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 22-Nov-1976
    Ending_Date: 21-Jun-2024
    Currentness_Reference:
    publication date of data used to train the model
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Model
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      Indirect_Spatial_Reference:
      Data were generated within a numerical model scheme. The model results presented are not for a particular geographic area.
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 1e-05. Longitudes are given to the nearest 1e-05. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is WGS84.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257.
  7. How does the data set describe geographic features?
    SMS-MetalML.zip
    zip folder containing all data and script files associated with the SMS machine learning model application for metal prediction. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/SMS-MetalML_Data_Dictionary.csv
    Data dictionary containing variable names, units, descriptions, and types for the full training data file: SMS-MetalML/1_KMeans/input_files/Input_dataset_250227.csv (Source: Producer defined) A metadata csv describing the attributes in the training data file found in SMS-MetalML/1_KMeans/input_files/Input-dataset_250227.csv.
    SMS-MetalML/Readme.md
    Markdown file for the overall SMS-MetalML model (Source: Producer defined) A markdown file with instructions on running the SMS-MetalML model workflow
    SMS-MetalML/1_KMeans
    Folder containing the KMeans++ clustering script and input/output files used to group samples by geochemical characteristics. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/1_KMeans/run_kmeans.py
    Python script to run KMeans++ clustering. Instructions are included for switching target metals (for example, Co, Zn, Au). (Source: Producer defined) Python script to run KMeans++ clustering
    SMS-MetalML/1_KMeans/Readme.md
    Markdown file for SMS-MetalML Part I K-Means++ Cluster Analysis (Source: Producer defined) A markdown file with instructions on running the K-Means++ Cluster Analysis step of the SMS-MetalML workflow
    SMS-MetalML/1_KMeans/KMeans_data_dictionary.csv
    Data dictionary describing input and output comma-delimited table headers unique to the 1_KMeans stage (Source: Producer defined) A metadata csv describing the model-generated unique attributes in comma-delimited tables from the code workflow for KMeans++ clustering.
    SMS-MetalML/1_KMeans/input_files
    Folder containing input files used to group samples by geochemical characteristics. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/1_KMeans/input_files/Input_dataset_250227.csv
    Working dataset used to train and apply the SMS-MetalML model for metal prediction (Source: Producer defined) Comma-delimited table containing input data for K-Means++ Cluster Analysis. Detailed attribute descriptions for all attributes are available in SMS-MetalML_Data_dictionary.csv.
    SMS-MetalML/1_KMeans/output_files
    Folder containing output files from the K-Means++ clustering. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/1_KMeans/output_files/cluster_centroids_Au_(ppb)_log10.csv
    Output centroid value for each cluster across scaled features. (Source: Producer defined) Comma-delimited table containing centroid values across scaled features for each cluster for the target metal gold (Au). Each row corresponds to a cluster, and each column corresponds to a scaled feature used in the clustering (such as, geochemical variables, depth, and one-hot encoded categorical features such as tectonic setting and spreading rate). Values are numeric and represent the feature’s centroid value for that cluster. See README.md in the 1_KMeans folder for the list and description of features.
    SMS-MetalML/1_KMeans/output_files/cluster_centroids_Co_(ppm)_log10.csv
    Output centroid value for each cluster across scaled features. (Source: Producer defined) Comma-delimited table containing centroid values across scaled features for each cluster for the target metal cobalt (Co). Each row corresponds to a cluster, and each column corresponds to a scaled feature used in the clustering (such as, geochemical variables, depth, and one-hot encoded categorical features such as tectonic setting and spreading rate). Values are numeric and represent the feature’s centroid value for that cluster. See README.md in the 1_KMeans folder for the list and description of features.
    SMS-MetalML/1_KMeans/output_files/cluster_centroids_Zn_(wt%)_log10.csv
    Output centroid value for each cluster across scaled features. (Source: Producer defined) Comma-delimited table containing centroid values across scaled features for each cluster for the target metal zinc (Zn). Each row corresponds to a cluster, and each column corresponds to a scaled feature used in the clustering (such as, geochemical variables, depth, and one-hot encoded categorical features such as tectonic setting and spreading rate). Values are numeric and represent the feature’s centroid value for that cluster. See README.md in the 1_KMeans folder for the list and description of features.
    SMS-MetalML/1_KMeans/output_files/df_with_clusters_Au_(ppb)_log10.csv
    Output cluster labels per K-Means++ sample analysis from input dataset. (Source: Producer defined) Comma-delimited table containing the input data with an additional Cluster column assigned by KMeans for the target metal gold (Au). Detailed attribute descriptions for the first 80 columns of this table are from the original source dataset (Input_dataset_250227.csv), and attributes are defined in SMS-MetalML_Data_dictionary.csv. Model-generated attributes unique to this file are documented in KMeans_data_dictionary.csv.
    SMS-MetalML/1_KMeans/output_files/df_with_clusters_Co_(ppm)_log10.csv
    Output cluster labels per K-Means++ sample analysis from input dataset. (Source: Producer defined) Comma-delimited table containing the input data with an additional Cluster column assigned by KMeans for the target metal cobalt (Co). Detailed attribute descriptions for the first 80 columns of this table are from the original source dataset (Input_dataset_250227.csv), and attributes are defined in SMS-MetalML_Data_dictionary.csv. Model-generated attributes unique to this file are documented in KMeans_data_dictionary.csv.
    SMS-MetalML/1_KMeans/output_files/df_with_clusters_Zn_(wt%)_log10.csv
    Output cluster labels per K-Means++ sample analysis from input dataset. (Source: Producer defined) Comma-delimited table containing the input data with an additional Cluster column assigned by KMeans for the target metal zinc (Zn). Detailed attribute descriptions for the first 80 columns of this table are from the original source dataset (Input_dataset_250227.csv), and attributes are defined in SMS-MetalML_Data_dictionary.csv. Model-generated attributes unique to this file are documented in KMeans_data_dictionary.csv.
    SMS-MetalML/2_Random Forest
    Folder containing Random Forest (RF) classifier scripts, input/output files, and pickled models for cobalt, gold, and zinc cluster prediction. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/2_Random Forest/RF_model.py
    Python script for training and testing the Random Forest model. Instructions included for switching target metals. (Source: Producer defined) Python script to train and test the Random Forest model
    SMS-MetalML/2_Random Forest/RF_predict_new_data.py
    Python script for deploying the Random Forest model to classify new data. (Source: Producer defined) Python script to train and test the Random Forest model
    SMS-MetalML/2_Random Forest/Readme.md
    Markdown file for SMS-MetalML Part II: Random Forest Classification (Source: Producer defined) A markdown file with instructions on running the Part II: Random Forest classification step of the SMS-MetalML workflow
    SMS-MetalML/2_Random Forest/RandomForest_data_dictionary.csv
    Data dictionary describing input and output comma-delimited table headers unique to the 2_Random Forest stage (Source: Producer defined) A metadata csv describing the model-generated unique attributes in comma-delimited tables from the code workflow for Random Forest classification.
    SMS-MetalML/2_Random Forest/RF_[element]_model.pkl
    Pickled Random Forest models for cobalt, zinc, and gold cluster prediction. (Source: Producer defined) Pickled Random Forest models, where [element] is either Co, Zn, or Au.
    SMS-MetalML/2_Random Forest/one_hot_encoder.pkl
    Pickled encoder for feature consistency checks before model application. (Source: Producer defined) Pickled encoder fitted to training data for feature consistency
    SMS-MetalML/2_Random Forest/input_files
    Folder containing Random Forest classifier input files. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/2_Random Forest/input_files/df_with_merged_clusters.csv
    Input dataset for RF_model.py containing the original KMeans input dataset (Input_dataset_250227.csv), cluster labels from the KMeans output, and metadata used for training. (Source: Producer defined) Comma-delimited table containing input data for Random Forest classification. Detailed attribute descriptions for the first 80 columns of this table are from the original KMeans source dataset (Input_dataset_250227.csv), and attributes are defined in SMS-MetalML_Data_dictionary.csv. Model-generated attributes unique to this file are documented in RandomForest_data_dictionary.csv.
    SMS-MetalML/2_Random Forest/input_files/Unmeasured_dataset_InterRidge_SMS.csv
    Input dataset for RF_predict_new_data.py, contains geophysical data from SMS deposits from the InterRidge Vents Database v3.4. Dataset contains spatial and tectonic data but no direct geochemical measurements. (Source: Producer defined) Comma-delimited table containing input data for Random Forest classification. Detailed attribute descriptions for this file are available at: https://doi.pangaea.de/10.1594/PANGAEA.917894.
    SMS-MetalML/2_Random Forest/output_files
    Folder containing Random Forest classifier output files. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/2_Random Forest/output_files/classification_results_[element].csv
    Output file containing predicted cluster labels and vote fractions for either gold (Au), cobalt (Co), or zinc (Zn). (Source: Producer defined) Comma-delimited table of test set results with predicted clusters and vote fractions. Detailed attribute descriptions for the first 83 columns of this table are from the Random Forest source dataset (df_with_merged_clusters.csv). Model-generated attributes unique to this file are documented in RandomForest_data_dictionary.csv.
    SMS-MetalML/2_Random Forest/output_files/InterRidge_SMS_RF-classified_[element].csv
    Output files from `RF_predict_new_data.py` for unmeasured input samples with assigned cluster labels and vote fractions. [element] is either gold (Au), cobalt (Co), or zinc (Zn). (Source: Producer defined) Comma-delimited table containing output from deployment script with predicted cluster labels for new data. The first 29 columns of this table are from the Random Forest source dataset (Unmeasured_dataset_InterRidge_SMS.csv). Detailed descriptions for these attributes are available at https://doi.pangaea.de/10.1594/PANGAEA.917894. Model-generated attributes unique to this file are documented in RandomForest_data_dictionary.csv.
    SMS-MetalML/3_XGBoost
    Folder containing XGBoost regression scripts, input/output files, and trained models to predict metal concentrations. (Source: U.S. Geological Survey)
    SMS-MetalML/3_XGBoost/run_xgb_model.py
    Python script to train and apply XGBoost regression for metal concentration prediction. Instructions included for changing metal targets. (Source: Producer defined) Python script to train and apply XGBoost regression
    SMS-MetalML/3_XGBoost/deploy_xgb_model.py
    Python script to deploy the XGBoost model to predict element concentrations on new data. (Source: This model archive) Python script to deploy the XGBoost model
    SMS-MetalML/3_XGBoost/Readme.md
    Markdown file for SMS-MetalML Part III: XGBoost Regression (Source: Producer defined) A markdown file with instructions on running the Part III: XGBoost Regression step of the SMS-MetalML workflow.
    SMS-MetalML/3_XGBoost/XGBoost_data_dictionary.csv
    Data dictionary for all 3_XGBoost input and output data. (Source: Producer defined) A metadata csv describing the unique attributes in all input and output files from the code workflow for XGBoost regression.
    SMS-MetalML/3_XGBoost/xgb_model_[element].pkl
    Pickled XGBoost regression models for cobalt, zinc, and gold concentration prediction. (Source: Producer defined) Pickled XGBoost regression model, where [element] is either Co, Zn, or Au.
    SMS-MetalML/3_XGBoost/selected_features_[element].pkl
    Pickled encoder for feature consistency checks before model application. (Source: Producer defined) Pickled encoder for feature consistency checks, where [element] is either Co, Zn, or Au.
    SMS-MetalML/3_XGBoost/input_files
    Folder containing XGBoost regression model input files. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/3_XGBoost/input_files/dataset_with_clusters_iqr_250416.csv
    Input data, including cluster labels from KMeans, for the XGBoost regression model. (Source: Producer defined) Input dataset filtered by interquartile range (IQR) and containing required features and cluster assignments. Columns 1, 5 to 83 of this comma-delimited table are described in SMS-MetalML_Data_Dictionary.csv. Columns 2 to 4 are the KMeans cluster class labels assigned for the Random Forest model input file df_with_merged_clusters.csv.
    SMS-MetalML/3_XGBoost/input_files/InterRidge_SMS_RF-classified_Co_Zn_Au.csv
    Input data for `deploy_xgb_model.py`. Dataset from InterRidge Vents Database v3.4--includes only vents that produce SMS deposits--. Includes the cluster class labeling assigned from the Random Forest model. (Source: Producer defined) Input dataset containing new SMS samples for concentration prediction. The first 29 columns of this table are from the Random Forest source dataset (Unmeasured_dataset_InterRidge_SMS.csv). Detailed descriptions for these attributes are available at https://doi.pangaea.de/10.1594/PANGAEA.917894.
    SMS-MetalML/3_XGBoost/output_files
    Folder containing XGBoost regression model output files. Attributes describe individual files within this folder. (Source: U.S. Geological Survey)
    SMS-MetalML/3_XGBoost/output_files/dataset_with_predictions_Au.csv
    Output dataset with predicted gold concentrations from the `run_xgb_model.py` script. (Source: Producer defined) Comma-delimited table containing predicted log-transformed metal concentrations and residuals for the test set. The first 83 columns of the table are from the input dataset for RF_model.py (dataset_with_clusters_iqr_250416.csv) with the Cluster_Au column omitted. Detailed attribute descriptions for all original source-data attributes are defined in SMS-MetalML_Data_dictionary.csv. Only model-generated attributes unique to this file are documented in XGBoost_data_dictionary.csv.
    SMS-MetalML/3_XGBoost/output_files/dataset_with_predictions_Co.csv
    Output dataset with predicted cobalt concentrations from the `run_xgb_model.py` script. (Source: Producer defined) Comma-delimited table containing predicted log-transformed metal concentrations and residuals for the test set. The first 83 columns of the table are from the input dataset for RF_model.py (dataset_with_clusters_iqr_250416.csv) with the Cluster_Co column omitted. Detailed attribute descriptions for all original source-data attributes are defined in SMS-MetalML_Data_dictionary.csv. Only model-generated attributes unique to this file are documented in XGBoost_data_dictionary.csv.
    SMS-MetalML/3_XGBoost/output_files/dataset_with_predictions_Zn.csv
    Output dataset with predicted zinc concentrations from the `run_xgb_model.py` script. (Source: Producer defined) Comma-delimited table containing predicted log-transformed metal concentrations and residuals for the test set. The first 83 columns of the table are from the input dataset for RF_model.py (dataset_with_clusters_iqr_250416.csv) with the Cluster_Zn column omitted. Detailed attribute descriptions for all original source-data attributes are defined in SMS-MetalML_Data_dictionary.csv. Only model-generated attributes unique to this file are documented in XGBoost_data_dictionary.csv.
    SMS-MetalML/3_XGBoost/output_files/predictions_Co_InterRidge_SMS_RF-classified_Co_Zn_Au.csv
    Output file from `deploy_xgb_model.py` containing predictions for new samples for cobalt. (Source: Producer defined) Comma-delimited table containing predicted concentrations for new SMS vent field samples. The first 32 columns are from the 3_XGBoost input file InterRidge_SMS_RF-classified_Co_Zn_Au.csv. Only model-generated attributes unique to this file are documented in XGBoost_data_dictionary.csv.
    SMS-MetalML/3_XGBoost/output_files/xgb_performance_Co.csv
    Output file from `run_xgb_model.py` containing RMSE and R squared values for cobalt. (Source: Producer defined) Summary table of RMSE (Root Mean Square Error) and R squared values.
    Entity_and_Attribute_Overview:
    Includes input and output files used in the SMS-MetalML model workflow. Supporting files include python scripts, pickled models, readme files, and data necessary for model deployment and reproducibility.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Maria C. Figueroa Matias
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

The purpose of this data release is to provide a machine learning framework and supporting files developed to estimate cobalt, gold, and zinc concentrations in seafloor massive sulfide (SMS) deposits. The model supports efforts to better understand geochemical variability and metal enrichment in SMS systems and to improve deep-sea mineral resource assessments across diverse tectonic settings.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 27-Feb-2025 (process 1 of 1)
    Details of processing steps contained within this release are below. For additional information, please see the relevant README.md within each folder. A multi-stage machine learning framework was developed to estimate cobalt, gold, and zinc concentrations in seafloor massive sulfide deposits. Workflow overview: (1) Cluster Analysis using K-Means++ (Folder: 1_KMeans): Forms geochemical cluster groups from a geochemical dataset (`dataset_250227.csv`). (2) Classification using Random Forest (Folder: 2_Random Forest): Assigns the geochemical clusters, from KMeans Cluster Analysis, to samples without geochemical data. (3) Regression using XGBoost (Folder: 3_XGBoost): Predicts metal concentrations using the geochemical clusters assigned from Random Forest and additional geophysical features (for example, depth, tectonic setting, spreading rate). This process was executed using Python 3.11.4, and all scripts, input files, and outputs necessary to replicate or apply the model are included in this model application data release. See the accompanying “SMS-MetalML_metadata_references.pdf” file in the Attached Files section for a full list of sources.
  3. What similar or related data should the user be aware of?

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    This model was developed using a machine learning approach that does not rely on traditional calibration targets. Instead, the model was trained and tested on a curated dataset of seafloor massive sulfide geochemical analyses. Accuracy was evaluated through standard machine learning methods, including cross-validation (10-fold) and performance metrics (silhouette scores, confusion matrices, root mean square error (RMSE), R squared) applied to a training and test set, split into 80/20.
  2. How accurate are the geographic locations?
    No formal positional accuracy tests were conducted, nor are they applicable.
  3. How accurate are the heights or depths?
    No formal positional accuracy tests were conducted, nor are they applicable.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
  5. How consistent are the relationships among the observations, including topology?
    Data were reviewed and processed to ensure consistency and minimize bias. Elemental concentrations were standardized across sources using unit conversion, as necessary, such that major elements were reported in weight percent (wt percent), while trace elements were expressed in parts per million (ppm) or parts per billion (ppb; such as, Au). Elemental concentrations below detection limits were imputed with half the detection limit value. Samples with a high proportion of weathering or gangue material were removed by applying the thresholds: Al2O3 less than 2.5 wt percent, and Ba less than 2 wt percent, and were further filtered to include only those representative of seafloor massive sulfide material by applying the threshold: S more than 10 wt percent. Columns with more than 55 percent missing values and rows with more than 40 percent missing values were excluded. Data were also balanced across vent sites by downsampling overrepresented locations and removing statistical outliers.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints No access constraints. Acknowledgment of the U.S. Geological Survey would be appreciated in products derived from this model application release.
Use_Constraints USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. These data are licensed under CC BY 4.0 and users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Please recognize and acknowledge the U.S. Geological Survey as the originator(s) of the dataset and in products derived from these data. Although the information contained in the model files may be useful for other purposes, it is incumbent on the user to understand the purpose, construction, and limitations of this model. This information is not intended for navigation purposes.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov
  2. What's the catalog number I need to order this data set? The models and supplementary documentation are contained in a single zip file (SMS-MetalML.zip) which also includes CSDGM FGDC compliant metadata.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
  4. How can I download or order the data?
    • Availability in digital form:
      Data format: Model data files including comma-delimited text, Python scripts, and Python pickle files (version Python 3.11.4, UTF-8, 2025, scikit-learn 1.2.2, XGBoost 1.7.5) Compressed archive containing all model scripts, input/output files, and documentation. Includes all scripts used to train, validate, and apply the machine learning model (KMeans++, Random Forest, and XGBoost), CSV files containing model-derived predictions of Co, Au, and Zn concentrations for vent fields, and Binary files containing trained machine learning models serialized with Python pickle format.
      Network links: https://www.sciencebase.gov/catalog/file/get/67f010a4d4be02766d636810
      https://doi.org/10.5066/P13PYBJL
    • Cost to order the data: None.

  5. What hardware or software do I need in order to use the data set?
    Python 3.11.4 is required to run the models.

Who wrote the metadata?

Dates:
Last modified: 16-Dec-2025
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/DataReleases/ScienceBase/DR_P13PYBJL/SMS-MetalML_metadata.faq.html>
Generated by mp version 2.9.51 on Mon Jan 12 12:51:45 2026