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Article . 2024
License: CC BY NC ND
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY NC ND
Data sources: Datacite
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Leveraging Machine Learning and Geophysical Data for Automated Detection of Interior Structures of Cratons

Authors: Hojat Shirmard; Ehsan Farahbakhsh; Karol Czarnota; R Dietmar Muller;

Leveraging Machine Learning and Geophysical Data for Automated Detection of Interior Structures of Cratons

Abstract

The internal structures and discontinuities of cratons hold considerable economic value due to their tendency for reactivation and different horizontal stress, serving as conduits for fluid flow and mineral deposition over time. Detecting these structures at various depths is critical for accurately mapping prospective zones of metallic mineralisation. This study demonstrates the effectiveness of integrating signal processing, feature extraction, and clustering on magnetic and gravity data for mapping the internal structures of the Gawler Craton, which has undergone rifting, sedimentation, extension, and orogenic processes. This combined approach results in precise internal structural mapping. Validated by three distinct metrics and geological maps, the resulting clustered maps can serve as foundational tools for further exploration and support decision-making in mineral exploration. Our findings indicate that most known metallic mineral occurrences, including all significant ones, are formed near the boundaries of these clusters. Therefore, mapping and targeting these boundaries can significantly reduce the search area for structurally controlled, extension-related mineral systems. Our proposed framework addresses the challenges of mapping hidden shallow and deep crustal structures, enhancing the capabilities of exploration geophysicists and geologists to investigate geological settings and the interiors of cratons. It provides a rapid, reliable, and cost-efficient method for generating geophysical features, which can be used as input to supervised prospectivity mapping workflows to identify favourable sites for mineralisation at any stage of an exploration program.

Related Organizations
Keywords

Feature extraction, Craton structures, Mineral exploration, Unsupervised machine learning, Clustering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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