Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Martin Blouin"'
Autor:
Maxime Claprood, Erwan Gloaguen, Thomas Béraud, Martin Blouin, Christian Dupuis, Philippe Ferron, Michel Ouellet, Michel Chaussé, Richard Martel, Daniel Paradis, Jean-Marc Ballard
Publikováno v:
Frontiers in Water, Vol 3 (2022)
Building a representative three-dimensional conceptual model of the hydrostratigraphic units is the critical first step when undertaking a hydrogeological assessment. The construction of such conceptual model requires integrating all geological data
Externí odkaz:
https://doaj.org/article/a7317e1c3a3142e4986dad1cb2cea588
Publikováno v:
Minerals, Vol 12, Iss 8, p 941 (2022)
Mineral prospectivity mapping (MPM), like other geoscience fields, is subject to a variety of uncertainties. When data about unfavorable sites to find deposits (i.e., drill intersections to barren rocks) is lacking in MPM using machine learning (ML)
Externí odkaz:
https://doaj.org/article/1c802478088e40a7b86aab1b79dfced0
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
Autor:
Martin Blouin, Erwan Gloaguen
Publikováno v:
The Leading Edge. 36:858-861
Whether it is deterministic, band-limited, or stochastic, seismic inversion can bear many names depending on the algorithm used to produce it. Broadly, inversion converts reflectivity data to physical properties of the earth, such as acoustic impedan
Publikováno v:
The Leading Edge. 36:215-219
Machine learning is becoming an appealing tool in various fields of earth sciences, especially in resources estimation. Six machine learning algorithms have been used to predict the presence of gold mineralization in drill core from geophysical logs
Autor:
Erwan Gloaguen, Martin Blouin
Publikováno v:
Journal of Environmental and Engineering Geophysics. 20:183-193
Accurate inference of the interfaces between geological units showing different hydraulic properties is a key step for a reliable hydrogeological characterization of regional aquifers. In this study, we developed a workflow that combines multiple geo
Publikováno v:
79th EAGE Conference and Exhibition 2017 - Workshops.
Machine learning is a popular topic in geosciences at the moment. It allows the management and interpretation of data in quantities and varieties (number of variables) that a human being would not be able to achieve. Rock physical properties acquired
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
Autor:
Michel Boulé, Martin Blouin
Publikováno v:
International Oil Spill Conference Proceedings. 2005:731-734
In the event of a marine oil spill, it is necessary to quickly and clearly assess the situation and estimate the extent of the area potentially impacted by oil. This software combines the following features integrated in a Geographical Information Sy