0de044a6-13b5-4675-9ff2-863ca84b5815 English MD_CharacterSetCode_utf8 Individual item (e.g. a one-off academic paper or single website) British Geological Survey Contact for the metadata enquiries@bgs.ac.uk pointOfContact 2024-11-07 ISO19115 2003/Cor.1:2006 A machine learning approach to geochemical mapping 2016-08-01 creation 2016-08-01 publication revision MICA WP3 2017-09-25T10:33:00 Record extracted from Batch 2 spreadsheet MICA_B2-3 https://www.mica-project.eu/ No additional information provided about the dataset A peer revied paper in Journal of Geochemical Exploration. Geochemical maps provide invaluable evidence to guide decisions on issues of mineral exploration, agriculture, and environmental health. However, the high cost of chemical analysis means that the ground sampling density will always be limited. Traditionally, geochemical maps have been produced through the interpolation of measured element concentrations between sample sites using models based on the spatial autocorrelation of data (e.g. semivariogram models for ordinary kriging). In their simplest form such models fail to consider potentially useful auxiliary information about the region and the accuracy of the maps may suffer as a result. In contrast, this study uses quantile regression forests (an elaboration of random forest) to investigate the potential of high resolution auxiliary information alone to support the generation of accurate and interpretable geochemical maps. This paper presents a summary of the performance of quantile regression forests in predicting element concentrations, loss on ignition and pH in the soils of south west England using high resolution remote sensing and geophysical survey data. Through stratified 10-fold cross validation we find the accuracy of quantile regression forests in predicting soil geochemistry in south west England to be a general improvement over that offered by ordinary kriging. Concentrations of immobile elements whose distributions are most tightly controlled by bedrock lithology are predicted with the greatest accuracy (e.g. Al with a cross-validated R2 of 0.79), while concentrations of more mobile elements prove harder to predict. In addition to providing a high level of prediction accuracy, models built on high resolution auxiliary variables allow for informative, process based, interpretations to be made. In conclusion, this study has highlighted the ability to map and understand the surface environment with greater accuracy and detail than previously possible by combining information from multiple datasets. As the quality and coverage of remote sensing and geophysical surveys continue to improve, machine learning methods will provide a means to interpret the otherwise-uninterpretable. Website: https://nora.nerc.ac.uk/id/eprint/513707/ to improve geochemical mapping techniques British Geological Survey Contact for the resource described by the metadata Nottingham NG125GG United Kingdom https://nora.nerc.ac.uk/id/eprint/513707/ WWW:LINK-1.0-http--link https://nora.nerc.ac.uk/id/eprint/513707/ Link for further information on the resource Party that supplies the resource https://resources.bgs.ac.uk/images/minerals/micaGraphic/B2/MICA_B2-3.JPG MICA dataCentre Recent (includes data from 2006 onwards) MICA ontology (TemporalScheme) 2 General descriptive information / Paper MICA ontology (DataScheme) 2 Requirement for data generation: Voluntary Method of data or information generation: Academic research Exploration GEMET Concepts 2014-07-25 revision 2 Geochemical GEMET Concepts 2014-07-25 revision 2 Geology GEMET Concepts 2014-07-25 revision 2 Geophysical GEMET Concepts 2014-07-25 revision 2 Gravity information GEMET Concepts 2014-07-25 revision 2 Mineralogical GEMET Concepts 2014-07-25 revision 2 Minerals GEMET Concepts 2014-07-25 revision 2 Sciences dealing with the composition, structure, origin of the Earth's rocks GEMET Concepts 2014-07-25 revision 2 Soils GEMET Concepts 2014-07-25 revision 2 Gold INSPIRE commodities Silver INSPIRE commodities Platinum INSPIRE commodities Palladium INSPIRE commodities Other platinum group metals (iridium) INSPIRE commodities Other platinum group metals (osmium) INSPIRE commodities Other platinum group metals (rhodium) INSPIRE commodities Other platinum group metals (ruthenium) INSPIRE commodities Aluminium INSPIRE commodities Copper INSPIRE commodities Lead INSPIRE commodities Tin INSPIRE commodities Zinc INSPIRE commodities Chromium INSPIRE commodities Cobalt INSPIRE commodities Iron INSPIRE commodities Manganese INSPIRE commodities Molybdenum INSPIRE commodities Nickel INSPIRE commodities Niobium INSPIRE commodities Tungsten INSPIRE commodities Vanadium INSPIRE commodities Antimony INSPIRE commodities Beryllium INSPIRE commodities Bismuth INSPIRE commodities Cadmium INSPIRE commodities Cesium INSPIRE commodities Gallium INSPIRE commodities Germanium INSPIRE commodities Hafnium INSPIRE commodities Indium INSPIRE commodities Lithium INSPIRE commodities Magnesium INSPIRE commodities Mercury INSPIRE commodities Rare earths (undifferentiated) INSPIRE commodities Rhenium INSPIRE commodities Rubidium INSPIRE commodities Selenium INSPIRE commodities Tantalum INSPIRE commodities Tellurium INSPIRE commodities Titanium INSPIRE commodities Zirconium INSPIRE commodities Detailed geochemistry MICA ontology (DomainScheme) 2 Geostatistical estimates MICA ontology (DomainScheme) 2 2D predictive mapping MICA ontology (DomainScheme) 2 Regional geochemistry MICA ontology (DomainScheme) 2 Available to subscribers only (who pay a fee) Not confidential English geoscientificInformation economy Soth West Region, United Kingdom Region within a country (please provide more details) Individual item (e.g. a one-off academic paper or single website) Data uncertainty Are any uncertainty measures provided (e.g. standard errors, confidence intervals, etc.)? true Quality assurance procedures Are quality assurance procedures described? true Information generation methods Are data or information generation methods formally described? true Record review Reviewed by: British Geological Survey Record validation false Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 Information provided on possible sources of digitisation error, etc. Metadata is distributed with a CC BY-ND 4.0 (Attribution-NoDerivs 4.0 International) licence