Airborne geophysical survey of northeastern Burkina Faso: New insights into the mineral potential of the Birimian basement

Bournas, Nasreddine; Plastow, Geoffrey; Han, Zuozhen; Latrous, Ali; Pitcher, Douglas

doi:10.1190/segam2016-13779128.1

Cited by 3 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several training areas surrounding known gold deposits were used to train the learning process. Bournas et al, (2016) showed that zones of higher probability correlate well with the auriferous Birimian volcanosedimentary belts that are affected or controlled by faults and shear zones and the arched structure of Goren and Djibo located in the northern part of Burkina Faso.…”

Section: Prospectivity Mappingmentioning

confidence: 97%

See 1 more Smart Citation

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

Bournas

Touré

Balboné

et al. 2019

ASEG Extended Abstracts

Self Cite

View full text Add to dashboard Cite

Section: Prospectivity Mappingmentioning

confidence: 97%

“…Gold targeting results for the north-eastern part of the country were previously derived from machine learning process (Bournas et al, 2016). Several training areas surrounding known gold deposits were used to train the learning process.…”

Section: Prospectivity Mappingmentioning

confidence: 99%

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

Bournas

Touré

Balboné

et al. 2019

ASEG Extended Abstracts

Self Cite

View full text Add to dashboard Cite

Predictive geologic mapping from geophysical data using self-organizing maps: A case study from Baie Verte, Newfoundland, Canada

Carter-McAuslan

Farquharson

2021

GEOPHYSICS

View full text Add to dashboard Cite

Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multi-variate datasets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We applied SOMs to magnetic, radiometric and gravity datasets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geological maps available for this area and the knowledge from numerous geological studies allowed for assessing the accuracy of the SOM-based predictive mapping. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geological units identified by previous traditional geological mapping. Of the combinations of datasets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the datasets. The SOM results could be used as input into k-means clustering as k-means clustering requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geological mapping would be most beneficial which can be especially useful in parts of the world where access is difficult and expensive.

show abstract

Impact of airborne geophysical surveys on the exploration of gold mineralization in Burkina Faso

Bournas

Touré

Balboné

et al. 2019

SEG Technical Program Expanded Abstracts 2019

View full text Add to dashboard Cite

Airborne geophysical survey of northeastern Burkina Faso: New insights into the mineral potential of the Birimian basement

Cited by 3 publications

References 5 publications

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

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

Predictive geologic mapping from geophysical data using self-organizing maps: A case study from Baie Verte, Newfoundland, Canada

Impact of airborne geophysical surveys on the exploration of gold mineralization in Burkina Faso

Contact Info

Product

Resources

About