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Improved supervised classification of bedrock in areas of transported overburden: applying domain expertise at Kerkasha, Eritrea

Citation

Hood, SB and Cracknell, MJ and Gazley, MF and Reading, AM, Improved supervised classification of bedrock in areas of transported overburden: applying domain expertise at Kerkasha, Eritrea, Applied Computing and Geosciences, 3-4 Article 100001. ISSN 2590-1974 (2019) [Refereed Article]


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Copyright Statement

2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1016/j.acags.2019.100001

Abstract

A regional bedrock map provides a foundation from which to build geological interpretations. However, rapid and accurate bedrock mapping in an area that lacks outcrop is a common problem, especially in regions with sparse data. A historic bedrock map from an Au and base metal project in the Kerkasha district, Eritrea, is significantly improved by predicting bedrock distribution in areas previously mapped as transported overburden. Publicly-available remote sensing data (DTM and ASTER) were combined with airborne geophysical data (magnetics and radiometrics) to provide features for bedrock prediction. Remote sensing data were pre-processed using Principal Components Analysis (PCA) to yield an equal number of principal components (PC) as input features. Four iterations were trialled, using different combinations of remote sensing PC features. The two initial trials used all available remote sensing data but compared results when feature ranking and selection is applied to reduce the number of PCs used for training and classification. The subsequent two trials used subsets of available remote-sensing data, selected based on domain expertise (i.e., the domain-specific knowledge of a geologist), with all respective PCs were retained. Five-fold cross-validation scores were highest when a DTM, magnetics, and radiometrics data were included as input features. However, qualitative visual appraisal of predicted results across trials, complemented by maps of class membership uncertainty (using a measure of entropy), indicate that geologically-meaningful results are also produced when radiometrics are omitted and only the DTM and magnetics are used. The study concludes with a generalised workflow to assist geologists who are seeking to improve the bedrock interpretation of areas under cover in a single area of interest. Domain expertise is shown to be critical for the selection of appropriate input features and validation of results during predictive lithologic mapping.

Item Details

Item Type:Refereed Article
Keywords:supervised classification, bedrock, overburden, Kerkasha, Eritrea
Research Division:Earth Sciences
Research Group:Geophysics
Research Field:Geophysics not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Earth Sciences
UTAS Author:Hood, SB (Mr Shawn Hood)
UTAS Author:Cracknell, MJ (Dr Matthew Cracknell)
UTAS Author:Reading, AM (Professor Anya Reading)
ID Code:136514
Year Published:2019
Funding Support:Australian Research Council (IH130200004)
Deposited By:Mathematics and Physics
Deposited On:2020-01-05
Last Modified:2020-05-20
Downloads:0

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