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

共 50 条

[31] STATISTICAL TECHNIQUES APPLIED TO BOREHOLE GEOPHYSICAL-DATA IN GOLD EXPLORATION
URBANCIC, TI
BAILEY, RC
[J]. GEOPHYSICAL PROSPECTING, 1988, 36 (07) : 752 - 771
[32] RECENT TRENDS IN GEOLOGICAL-GEOPHYSICAL EXPLORATION, AND METHODS OF IMPROVING THE USE OF GEOPHYSICAL DATA
COFFIN, RC
[J]. AAPG BULLETIN-AMERICAN ASSOCIATION OF PETROLEUM GEOLOGISTS, 1946, 30 (05): : 748 - 748
[33] Geophysical Contributions to Gold Exploration in Western Mali According to Airborne Electromagnetic Data Interpretations
Kone, Adama Youssouf
Nasr, Imen Hamdi
Traore, Baco
Amiri, Adnen
Inoubli, Mohamed Hedi
Sangare, Souleymane
Qaysi, Saleh
[J]. MINERALS, 2021, 11 (02) : 1 - 15
[34] RECENT TRENDS IN GEOLOGICAL-GEOPHYSICAL EXPLORATION AND METHODS OF IMPROVING USE OF GEOPHYSICAL DATA
COFFIN, RC
[J]. AAPG BULLETIN-AMERICAN ASSOCIATION OF PETROLEUM GEOLOGISTS, 1946, 30 (12): : 2013 - 2033
[35] A Comprehensive Exploration to the Machine Learning Techniques for Diabetes Identification
Wei, Sidong
Zhao, Xuejiao
Miao, Chunyan
[J]. 2018 IEEE 4TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT), 2018, : 291 - 295
[36] Exploration of Biodegradable Substances Using Machine Learning Techniques
Elsayad, Alaa M.
Zeghid, Medien
Ahmed, Hassan Yousif
Elsayad, Khaled A.
[J]. SUSTAINABILITY, 2023, 15 (17)
[37] An exploration on text classification using machine learning techniques
Athanasios, Tzimourtas
Spyros, Bakalakos
Panagiota, Tselenti
Athanasios, Voulodimos
[J]. 25TH PAN-HELLENIC CONFERENCE ON INFORMATICS WITH INTERNATIONAL PARTICIPATION (PCI2021), 2021, : 247 - 249
[38] Airborne particulate matter measurement and prediction with machine learning techniques
Iwaszenko, Sebastian
Smolinski, Adam
Grzanka, Marcin
Skowronek, Tomasz
[J]. SCIENTIFIC REPORTS, 2024, 14 (01):
[39] Injury Prediction for Canadian Mineral Exploration Using Machine Learning
Saffarvarkiani, Elmira
Passi, Kalpdrum
Godwin, Alison
[J]. 2024 IEEE CONFERENCE ON COGNITIVE AND COMPUTATIONAL ASPECTS OF SITUATION MANAGEMENT, COGSIMA, 2024, : 127 - 131
[40] Rapid integration of large airborne geophysical data suites using a fuzzy partitioning cluster algorithm: a tool for geological mapping and mineral exploration targeting
Paasche, Hendrik
Eberle, Detlef G.
[J]. EXPLORATION GEOPHYSICS, 2009, 40 (03) : 277 - 287

← 1 2 3 4 5 →