Identification

Title

Random Forest model output prediction raster for the occurrence of Fe-Mn in the World Oceans

Abstract

The raster provide the output of a machine-learning random forest algorithm modelling the occurrence of ferromanganese (Fe-Mn) crust deposits in the world ocean. This raster constitutes a data-driven approach for mineral prospectivity mapping of Fe-Mn crusts that should be used in conjunction with other expert-driven prospectivity analysis to guide the assessment of Fe-Mn crust coverage in the world ocean and potential mineral exploration. The raster contains values between 0.07 and 0.92. Any values outside of that range (e.g., 0) are outside of the model prediction and should not be displayed. To reproduce data as displayed in the forthcoming associated publication, it is recommended to apply a 'Percent Clip' stretched 'Viridis' colour scheme.

Resource type

nonGeographicDataset

Resource locator

https://webapps.bgs.ac.uk/services/ngdc/accessions/index.html#item176384

name: Data

function: download

https://doi.org/10.5285/4c8419b9-5ee4-4db4-b279-18d3ec75c3c4

name: Digital Object Identifier (DOI)

function: information

Unique resource identifier

code

http://data.bgs.ac.uk/id/dataHolding/13607985

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

originating controlled vocabulary

title

reference date

date type

publication

effective date

2008-06-01

Keyword set

keyword value

Mineral exploration

Submarine mineral deposits

Ferromanganese

Earth crust

originating controlled vocabulary

title

reference date

date type

revision

effective date

2011

Keyword set

keyword value

NERC_DDC

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

2020

End position

2022

Dataset reference date

date type

creation

effective date

2022-09-28

Frequency of update

notPlanned

Quality and validity

Lineage

The raster dataset correspond to the output of a random-forest algorithm data classification. The model uses a set of known locations for deposit and non-deposit samples to fingerprint the geospatial signature of each class against a range of environmental and geological variables, also referred to as predictors. The multivariate geospatial signature of each pixel covered by all predictors is then used to predict the likelihood of occurrence of the target class. This prediction takes the form of a raster data set with value between 0 and 1 marking the confidence in the prediction of the target class.

Conformity

Conformity report

specification

title

reference date

date type

publication

effective date

2011

degree

false

explanation

See the referenced specification

Conformity report

specification

title

reference date

date type

publication

effective date

2010-12-08

degree

false

explanation

See http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2010:323:0011:0102:EN:PDF

Data format

name of format

raster

version of format

Constraints related to access and use

Constraint set

Limitations on public access

Constraint set

Limitations on public access

The copyright of materials derived from the British Geological Survey's work is vested in the Natural Environment Research Council [NERC]. No part of this work may be reproduced or transmitted in any form or by any means, or stored in a retrieval system of any nature, without the prior permission of the copyright holder, via the BGS Intellectual Property Rights Manager. Use by customers of information provided by the BGS, is at the customer's own risk. In view of the disparate sources of information at BGS's disposal, including such material donated to BGS, that BGS accepts in good faith as being accurate, the Natural Environment Research Council (NERC) gives no warranty, expressed or implied, as to the quality or accuracy of the information supplied, or to the information's suitability for any use. NERC/BGS accepts no liability whatever in respect of loss, damage, injury or other occurence however caused.

Responsible organisations

Responsible party

organisation name

British Geological Survey

full postal address

Environmental Science Centre, Nicker Hill, Keyworth

NOTTINGHAM

NG12 5GG

United Kingdom

telephone number

0115 936 3143

facsimile number

0115 936 3276

email address

enquiries@bgs.ac.uk

responsible party role

distributor

Responsible party

organisation name

British Geological Survey

full postal address

Environmental Science Centre, Nicker Hill, Keyworth

NOTTINGHAM

NG12 5GG

United Kingdom

telephone number

0115 936 3143

facsimile number

0115 936 3276

email address

enquiries@bgs.ac.uk

responsible party role

originator

Responsible party

organisation name

British Geological Survey

email address

not available

responsible party role

distributor

Responsible party

organisation name

British Geological Survey

email address

not available

responsible party role

originator

Responsible party

organisation name

British Geological Survey

email address

not available

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact

organisation name

British Geological Survey

full postal address

Environmental Science Centre, Keyworth

NOTTINGHAM

NG12 5GG

United Kingdom

telephone number

+44 115 936 3100

email address

enquiries@bgs.ac.uk

responsible party role

pointOfContact

Metadata date

2023-03-22

Metadata language