Use of machine learning techniques on airborne geophysical data for mineral resources exploration in Burkina Faso

被引：0

作者：

Bournas N. ^{[1
]}

Touré A. ^{[2
,3
]}

Balboné M. ^{[2
,3
]}

Zagré P.S. ^{[2
,3
]}

Ouédraogo A. ^{[3
]}

Khaled K. ^{[1
]}

Prikhodko A. ^{[1
]}

Legault J. ^{[1
]}

机构：

[1] Geotech Ltd, Aurora, ON

来源：

Exploration Geophysics | 2019年 / 2019卷 / 01期

关键词：

airborne geophysics; Burkina Faso; machine learning; maximum likelihood classifier;

D O I：

10.1080/22020586.2019.12072949

中图分类号：

学科分类号：

摘要：

Recent advances in development of automated tools and machine learning algorithms based on artificial intelligence (AI) have revolutionized our interpretation approach of big data by making it faster, more objective and more reliable than tedious manual processes. In this paper, we show results derived from machine learning applications to the recently acquired high-resolution airborne geophysical data of Burkina Faso. The results are represented as country-wide prospectivity maps for various mineral resources including gold, uranium, base metals and strategic metals. The new mapping products indicate that Burkina Faso has a diversified and significant mineral potential. © 2019, Taylor and Francis. All rights reserved.

引用

页码：1 / 4

页数：3

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