Product catalogue
Surficial and deep earth material prediction from geochemical compositions - a spatial predictive model
Prediction of true classes of surficial and deep earth materials using multivariate geospatial data is a common challenge for geoscience modellers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.
Simple
Identification info
- Date (Creation)
- 2018-08-27T09:00:00
- Date (Publication)
- 2023-10-26T06:24:02
- Citation identifier
- Geoscience Australia Persistent Identifier/https://pid.geoscience.gov.au/dataset/ga/122495
- Cited responsible party
-
Role Organisation / Individual Name Details Author Talebi, H.
Author Mueller, U.
Author Tolosana-Delgado, R.
Author Grunsky, E.
Author McKinley, J.M.
Author de Caritat, P.
Publisher Springer Nature
External Contact
- Name
-
Natural Resources Research
- Issue identification
-
Volume 28, 2019
- Page
-
869-891
- Purpose
-
Article for submission to Natural Resources Research journal
- Status
- Completed
- Point of contact
-
Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Resource provider Minerals, Energy and Groundwater Division
External Contact Point of contact Main, P.
MEG Internal Contact
- Spatial representation type
- Topic category
-
- Geoscientific information
Extent
Extent
))
- Maintenance and update frequency
- As needed
Resource format
- Title
-
Product data repository: Various Formats
- Website
-
Data Store directory containing the digital product files
Data Store directory containing one or more files, possibly in a variety of formats, accessible to Geoscience Australia staff only for internal purposes
- theme.ANZRC Fields of Research.rdf
-
-
EARTH SCIENCES
-
- Keywords
-
-
Compositional data
-
- Keywords
-
-
Log-ratio
-
- Keywords
-
-
flow anamorphosis
-
- Keywords
-
-
geostatistical simulation
-
- Keywords
-
-
machine learning
-
- Keywords
-
-
Published_External
-
Resource constraints
- Title
-
Creative Commons Attribution 4.0 International Licence
- Alternate title
-
CC-BY
- Edition
-
4.0
- Access constraints
- License
- Use constraints
- License
- Other constraints
-
(c) 2018 The Author(s)
Resource constraints
- Title
-
Australian Government Security ClassificationSystem
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
- Language
- English
- Character encoding
- UTF8
Distribution Information
- Distributor contact
-
Role Organisation / Individual Name Details Distributor Commonwealth of Australia (Geoscience Australia)
Voice facsimile
- OnLine resource
-
Link to Journal
Link to Journal
- Distribution format
-
Resource lineage
- Statement
-
Article for submission to Natural Resources Research journal
- Hierarchy level
- Document
Metadata constraints
- Title
-
Australian Government Security ClassificationSystem
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Metadata
- Metadata identifier
-
urn:uuid/8e76cf53-dd98-4361-abe4-0b72612c3ebe
- Title
-
GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
- Contact
-
Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact Main, P.
MEG Internal Contact
Type of resource
- Resource scope
- Document
- Name
-
Journal Articles and Conference Papers
Alternative metadata reference
- Title
-
Geoscience Australia - short identifier for metadata record with
uuid
- Citation identifier
- eCatId/122495
- Date info (Creation)
- 2018-05-04T03:37:56
- Date info (Revision)
- 2018-05-04T03:38:12
Metadata standard
- Title
-
AU/NZS ISO 19115-1:2014
Metadata standard
- Title
-
ISO 19115-1:2014
Metadata standard
- Title
-
ISO 19115-3
- Title
-
Geoscience Australia Community Metadata Profile of ISO 19115-1:2014
- Edition
-
Version 2.0, September 2018
- Citation identifier
- https://pid.geoscience.gov.au/dataset/ga/122551