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Image of Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Qu ebec and Labrador

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Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Qu ebec and Labrador

Steven E. Zhang - Nama Orang; Glen T. Nwaila - Nama Orang; Julie E. Bourdeau - Nama Orang; David Corrigan - Nama Orang;

Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limitations are the dependence on sample stoichiometry (e.g., the existence of minerals), the necessity of log-ratio transformations when dealing with compositional data, and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping. In this study, we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML. We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province (Québec and Labrador), Canada. The region is known for its REEs endowment and the data were gathered for prospectivity mapping. A comparison with the established multivariate hybrid data- and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort, our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications. The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region. These findings have potentially wider implications on exploration target generation, where project risks (financial, environmental, political, etc.) and geochemical anomalies must be quantified using robust and effective data-driven approaches. In addition, our methodology is more replicable and objective, as manual geoscientific interpretation is not required during the detection of geochemical anomalies.


Ketersediaan
269551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd.., 2021
Deskripsi Fisik
20 hlm PDF, 16.632 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.2, December 2021
Subjek
Principal component analysis
ML
Mineral prospectivity mapping
Geochemical anomaly
REEs
Info Detail Spesifik
-
Pernyataan Tanggungjawab
-
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Qu ebec and Labrador
    Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limitations are the dependence on sample stoichiometry (e.g., the existence of minerals), the necessity of log-ratio transformations when dealing with compositional data, and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping. In this study, we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML. We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province (Québec and Labrador), Canada. The region is known for its REEs endowment and the data were gathered for prospectivity mapping. A comparison with the established multivariate hybrid data- and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort, our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications. The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region. These findings have potentially wider implications on exploration target generation, where project risks (financial, environmental, political, etc.) and geochemical anomalies must be quantified using robust and effective data-driven approaches. In addition, our methodology is more replicable and objective, as manual geoscientific interpretation is not required during the detection of geochemical anomalies.
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