Product catalogue
AEM Assist: A national machine learning tool for airborne electromagnetic interpretation and extrapolation
<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>Airborne electromagnetic surveys are widely used in Australia for mineral exploration, groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). In the last decade, regional surveys have been acquired covering approximately two thirds of the continent and resulting in a large volume of data to interpret. To address this challenge, we have developed a machine learning workflow to assist with the interpretation of AEM conductivity depth sections.</div><div>‘AEM assist’ is an open-source machine learning algorithm that allows the user to interpret AEM sections from drillhole observations and/or interpreted segments along the conductivity depth section. AEM assist finds predictive relationships between the training observations (drillhole and/or interpreted sections) and the conductivity value which also includes the first vertical derivative of the conductivity. Due to the non-uniqueness of the conductivity response, we have also built in a suite of supplementary covariates or features to help improve the model prediction. These features include terrain indices, gamma radiometric, surface weathering intensity, distance proxies (e.g., distance from rocks of a known age), climate indices, gravity, and magnetic derivatives. We have built the AEM assist into a national mapping framework to facilitate model interpretation and training anywhere in Australia. Although local training of sections is recommended the national framework provides an opportunity to train a model in one region and predict into another area given similar geological and landscape histories. The AEM assist has the potential to speed up the interpretation of AEM flightline sections with statistical models of interpretation uncertainty. AEM assist can be used to provide a first pass interpretation of a survey area that can later be revised by the domain expert. A feature of AEM assist is that it systematically integrates many datasets that would otherwise be difficult to do from traditional methods.</div><div><br></div><div><strong>Citation:</strong> Basak S., Wilford J., Wong S.C.T., Ley-Cooper Y. & Ray A., 2024. AEM assist - a national predictive machine learning framework for airborne electromagnetic interpretation and extrapolation. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra, https://doi.org/10.26186/149495</div>
Simple
Identification info
- Date (Creation)
- 2024-04-27T08:00:00
- Date (Publication)
- 2025-01-24T02:54:53
- Citation identifier
- Geoscience Australia Persistent Identifier/https://pid.geoscience.gov.au/dataset/ga/149495
- Citation identifier
- Digital Object Identifier/https://dx.doi.org/10.26186/149495
- Cited responsible party
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Role Organisation / Individual Name Details Publisher Commonwealth of Australia (Geoscience Australia)
Voice Author Wilford, J.
External Contact Author Basak, S.
External Contact Author Wong, S.
Internal Contact Author LeyCooper, Y.
Internal Contact Author Ray, A.
Internal Contact Editor Czarnota, K.
Internal Contact
- Name
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EFTF extended abstracts
- Purpose
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improving the interpretation of AEM survey datasets
- Status
- Completed
- Point of contact
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Role Organisation / Individual Name Details Resource provider Minerals, Energy and Groundwater Division
External Contact Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact LeyCooper, Y.
Internal Contact
- Spatial representation type
- Topic category
-
- Geoscientific information
Extent
))
- Maintenance and update frequency
- As needed
Resource format
- Title
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Product data repository: Various Formats
- Website
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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
- Project
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EFTF – Exploring for the Future
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- Keywords
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AusAEM interpretation
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- Keywords
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machine learning
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- Keywords
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numerical modelling
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- Keywords
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covaraites
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- theme.ANZRC Fields of Research.rdf
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Electrical and electromagnetic methods in geophysics
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- Keywords
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Published_External
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Resource constraints
- Addressee
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Role Organisation / Individual Name Details User Any
- Use constraints
- License
- Use constraints
- Other restrictions
- Other constraints
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© Commonwealth of Australia (Geoscience Australia) 2024
Resource constraints
- Title
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Australian Government Security Classification System
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
- Classification system
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Australian Government Security Classification System
- Language
- English
- Character encoding
- UTF8
Distribution Information
- Distributor contact
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Role Organisation / Individual Name Details Distributor Commonwealth of Australia (Geoscience Australia)
Voice facsimile
- OnLine resource
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Extended Abstract for download (pdf) [1.7 MB]
Extended Abstract for download (pdf) [1.7 MB]
- Distribution format
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pdf
- File decompression technique
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nil
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Resource lineage
- Statement
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<div>This publication was produced as part of Geoscience Australia's Exploring for the Future Program. It was presented at the Exploring for the Future 2024 Showcase.</div>
Metadata constraints
- Title
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Australian Government Security Classification System
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Metadata
- Metadata identifier
-
urn:uuid/9090de3e-1edc-47f3-a8c0-cd13c73972bc
- Title
-
GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
- Contact
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Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact LeyCooper, Y.
Internal Contact
Type of resource
- Resource scope
- Document
- Name
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GA Abstract / Fact Sheet
Alternative metadata reference
- Title
-
Geoscience Australia - short identifier for metadata record with
uuid
- Citation identifier
- eCatId/149495
- Date info (Creation)
- 2024-08-23T04:55:11
- Date info (Revision)
- 2024-08-23T04:55:11
Metadata standard
- Title
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AU/NZS ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-3
- Title
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Geoscience Australia Community Metadata Profile of ISO 19115-1:2014
- Edition
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Version 2.0, September 2018
- Citation identifier
- http://pid.geoscience.gov.au/dataset/ga/122551