Výsledky vyhledávání - "Julie E. Bourdeau"

Akademický článek

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Autor: Steven E. Zhang, Glen T. Nwaila, Shenelle Agard, Julie E. Bourdeau, Emmanuel John M. Carranza, Yousef Ghorbani

Publikováno v: Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 137-149 (2023)

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This directi

Externí odkaz: https://doaj.org/article/3f12ef98690a45ab9673b30bd1e1666f

Zobrazit plný text záznamu

Akademický článek

Deriving big geochemical data from high-resolution remote sensing data via machine learning: Application to a tailing storage facility in the Witwatersrand goldfields

Autor: Steven E. Zhang, Glen T. Nwaila, Julie E. Bourdeau, Yousef Ghorbani, Emmanuel John M. Carranza

Publikováno v: Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 9-21 (2023)

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites,

Externí odkaz: https://doaj.org/article/8c2410056b5241adb33d298d8ab061f1

Zobrazit plný text záznamu

Akademický článek

Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager

Autor: Glen T. Nwaila, Steven E. Zhang, Julie E. Bourdeau, Yousef Ghorbani, Emmanuel John M. Carranza

Publikováno v: Artificial Intelligence in Geosciences, Vol 3, Iss , Pp 71-85 (2022)

Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical and geophysical surveys, and/or remote sensing

Externí odkaz: https://doaj.org/article/214630e2ad9347879d34410869b8635d

Zobrazit plný text záznamu

Akademický článek

Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns

Autor: Steven E. Zhang, Julie E. Bourdeau, Glen T. Nwaila, Yousef Ghorbani

Publikováno v: Artificial Intelligence in Geosciences, Vol 3, Iss , Pp 86-100 (2022)

In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more

Externí odkaz: https://doaj.org/article/245240c804bc4689b55a0a32a83e1840

Zobrazit plný text záznamu

Akademický článek

Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

Autor: Steven E. Zhang, Glen T. Nwaila, Julie E. Bourdeau, Lewis D. Ashwal

Publikováno v: Artificial Intelligence in Geosciences, Vol 2, Iss , Pp 60-75 (2021)

In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwa

Externí odkaz: https://doaj.org/article/b9684f172d3c466a8701cbcc3fa3f260

Zobrazit plný text záznamu

Akademický článek

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

Autor: Steven E. Zhang, Julie E. Bourdeau, Glen T. Nwaila, David Corrigan

Publikováno v: Artificial Intelligence in Geosciences, Vol 2, Iss , Pp 128-147 (2021)

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 informatio

Externí odkaz: https://doaj.org/article/2ff3b62c3ea9487e9d7d9b36193109af

Zobrazit plný text záznamu

Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields

Autor: Steven E. Zhang, Glen T. Nwaila, Julie E. Bourdeau, Yousef Ghorbani, Emmanuel John M. Carranza

Publikováno v: Natural Resources Research. 32:879-900

Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3de2d2ad8ec812db6026f5461bfb1f28
https://doi.org/10.1007/s11053-023-10159-7

Zobrazit plný text záznamu

Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

Autor: Glen T. Nwaila, Steven E. Zhang, Julie E. Bourdeau, Elekanyani Negwangwatini, Derek H. Rose, Mark Burnett, Yousef Ghorbani

Publikováno v: Natural Resources Research. 31:2369-2395

The Assen Fe ore deposit is a banded iron formation (BIF)-hosted orebody, occurring in the Penge Formation of the Transvaal Supergroup, located 50 km northwest of Pretoria in South Africa. Most BIF-hosted Fe ore deposits have experienced post-deposit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::62f5491fd076111e2e703ef34bab5646
https://doi.org/10.1007/s11053-022-10105-z

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání