Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Shiva Tirdad"'
Autor:
Matthieu, Cedou, Erwan, Gloaguen, Martin, Blouin, Antoine, Caté, Jean-Philippe, Paiement, Shiva, Tirdad
Airborne magnetic data are commonly used to produce preliminary geological maps. Machine learning has the potential to partly fulfill this task rapidly and objectively, as geological mapping is comparable to a semantic segmentation problem. Because t
Externí odkaz:
http://arxiv.org/abs/2110.14440
Autor:
Mojtaba Bavandsavadkoohi, Matthieu Cedou, Martin Blouin, Erwan Gloaguen, Shiva Tirdad, Bernard Giroux
Publikováno v:
Computers & Geosciences. 176:105363
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa4c42a920bca7f9aeba9b5c1ef1e243
https://doi.org/10.1016/j.cageo.2023.105363
https://doi.org/10.1016/j.cageo.2023.105363
Publikováno v:
Canadian Journal of Earth Sciences. 56:556-568
We propose a new numerical workflow based on stochastic data integration where we merge a conceptual geological model, drillhole geophysical and geological logs, and surface geophysical data to compute a unified numerical model of a volcanogenic mass
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eff4df552ce8e6e3ff9a2eb53c0ca7c4
https://doi.org/10.1139/cjes-2018-0166
https://doi.org/10.1139/cjes-2018-0166
Publikováno v:
Proceedings.
Summary This work presents the first step of the use of a Bayesian sequential simulation algorithm to estimate the iron grades of Lalor VMS deposit. The approach is based on an in situ petrophysical relationship between iron grades and both conductiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e6686688a24f5b1b4bcdd7e5c6a2047
https://doi.org/10.3997/2214-4609.201602139
https://doi.org/10.3997/2214-4609.201602139