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Geological mapping using extreme gradient boosting and the deep neural networks: application to silet area, central Hoggar, Algeria


Nowadays, machine learning algorithms are considered a powerful tool for analyzing big and complex data due to their ability to deliver accurate and fast results. The main objective of the present study is to prove the effectiveness of the extreme gradient boosting (XGBoost) method as well as employed data types in the Saharan region mapping. To reveal the potential of the XGBoost, we conducted two experiments. The first was to use different combinations of: airborne gamma-ray spectrometry data, airborne magnetic data, Landsat 8 data and digital elevation model. The objective is to train 9 XGBoost models in order to determine each data type sensitivity in capturing the lithological rock classes. The second experiment was to compare the XGBoost to deep neural networks (DNN) to display its potential against other machine learning algorithms. Compared to the existing geological map, the application of XGBoost reveals a great potential for geological mapping as it was able to achieve a correlation score of (78%) where igneous and metamorphic rocks are easily identified compared to sedimentary rocks. In addition, using different data combinations reveals airborne magnetic data utility to discriminate some lithological units. It also reveals the potential of the apparent density, derived from airborne magnetic data, to improve the algorithm’s accuracy up to 20%. Furthermore, the second experiment in this study indicates that the XGBoost is a better choice for the geological mapping task compared to the DNN. The obtained predicted map shows that the XGBoost method provides an efficient tool to update existing geological maps and to edit new geological maps in the region with well outcropped rocks.

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This research was supported by the Laboratoire de Physique de la Terre (LABOPHYT), University M’hamed Bougara of Boumerdes and the Directorate General for Scientific Research and Technological Development (DGRSDT) of the Ministry of Higher Education and Scientific Research of Algeria. A. A. Elbegue (first author) expresses his sincere thanks to COMENA for giving him the opportunity to follow an internship with their team especially Dr D.Groune.


Both data sharing and funding are not applicable to this study. However, this research was supported by the Laboratoire de Physique de la Terre (LABOPHYT) from the University of Boumerdes.

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All the authors, namely Abderrahmane Aref Elbegue, Karim Allek and Hocine Zeghouane, hereby declare their consent to have participated in and to publish this study. This study is a result of both direct and indirect contributions of the authors listed whereby each author’s expertise was necessary to conduct research. All authors have participated in the conception, the design and the critical revision of the paper within the framework of intellectual content until approval of the final version was reached.

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Correspondence to Abderrahmane Aref Elbegue.

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Edited by Dr. Rafał Czarny (ASSOCIATE EDITOR) / Prof. Gabriela Fernández Viejo (CO-EDITOR-IN-CHIEF).



See Table 5.

Table 5 The confusion matrix of the XGBoost trained with all data. The diagonal value represents the recall value related to each rock types, and the off-diagonal represent the ratio of the observations that were predicted as another rock type

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Elbegue, A.A., Allek, K. & Zeghouane, H. Geological mapping using extreme gradient boosting and the deep neural networks: application to silet area, central Hoggar, Algeria. Acta Geophys. 70, 1581–1599 (2022).

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  • Machine learning
  • Geological mapping
  • Airborne geophysical data
  • Landsat8
  • XGBoost
  • Hoggar