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Application of geophysical and multispectral imagery data for predictive mapping of a complex geo-tectonic unit: a case study of the East Vardar Ophiolite Zone, North-Macedonia

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Abstract

The Random Forest (RF) and K nearest neighbors (KNN) machine learning (ML) algorithms were evaluated for their ability to predict ophiolite occurrences, in the East Vardar Zone (EVZ) of central North Macedonia. A predictive map of the investigated area was created using three data sources: geophysical data (digital elevation model, gravity and geomagnetic), multispectral optical satellite images (Landsat 7 ETM + and their derivatives), and geological data (distance to fault map and ophiolite outcrops map). The research included a comparison and discussion on the statistical and geological findings derived from different training dataset class ratios in relation to a testing dataset characterized by significant class imbalance. The results suggest that the precise selection of a suitable class balance for the training dataset is a critical factor in achieving accurate ophiolite prediction with RF and KNN algorithms. The analysis of feature importance revealed that the Bouguer gravity anomaly map, total intensity of the Earth’s magnetic field reduced to the pole map, distance to fault map, band ratio BR3 map obtained from multispectral satellite images, and digital elevation model are the most significant features for predicting ophiolites within the EVZ. KNN showed poorer results compared to RF in terms of both the evaluation metrics and visual analysis of prediction maps. The methods applied in this research can be applied for predictive mapping of complex geo-tectonic units covered by dense vegetation, and may indicate the presence of these units even if they were not previously mapped, particularly when geophysical data are used as features.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The study was funded by the Ministry of Education, Science and Technological Development, Republic of Serbia under the "Agreement on the realization and financing of scientific research work in 2023": grant no. 451–03-47/2023–01/200126 (Faculty of Mining and Geology) and grant no. 451–03-68/2020–14/200092 (University of Belgrade, Faculty of Civil Engineering).

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Authors and Affiliations

  1. University of Belgrade, Institute of Physics Belgrade, Pregrevica 118, 11080, Belgrade, Serbia

    Filip Arnaut

  2. University of Belgrade, Faculty of Mining and Geology, Đušina 7, 11000, Belgrade, Serbia

    Dragana Đurić

  3. University of Belgrade, Faculty of Civil Engineering, Bulevar Kralja Aleksandra 73/1, 11000, Belgrade, Serbia

    Uroš Đurić & Mileva Samardžić-Petrović

  4. Ss. Cyril and Methodius University in Skopje, Faculty of Civil Engineering MK, Bulevar Partizanski Odredi 24, 1000, Skopje, North Macedonia

    Igor Peshevski

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Contributions

This research project represents a collective effort in which each author played a crucial role in shaping its conception, methodology, and design. The initial idea was ignited by Dragana Đurić and Uroš Đurić, who laid the foundation for the study by developing the very first models and utilizing machine learning algorithms. Subsequently, Mileva Samardžić-Petrović took charge of the statistical analyses and delved into the initial exploration of machine learning algorithms. Building upon this groundwork, Filip Arnaut made significant contributions and novelty by enhancing the machine learning techniques, ultimately introducing the novel application of KNN, which led to the development of entirely new models and enriched the analysis. Moreover, the research was complemented by the invaluable expertise of Igor Peshevski, who conducted an in-depth literature review focusing on geological and tectonic aspects, contributing to a more comprehensive understanding of the context. The collaborative nature of this endeavor is exemplified in the writing process, wherein Filip Arnaut took the lead in crafting the manuscript while benefiting from substantial contributions and assistance from Dragana Đurić, Uroš Đurić, Mileva Samardžić-Petrović, and Igor Peshevski. Throughout this stage, the manuscript underwent internal review process, with each author providing valuable comments and feedback on earlier versions. The culmination of this collaborative effort is the final manuscript, which has been thoroughly reviewed, refined, and approved by all authors.

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Correspondence to Filip Arnaut.

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Arnaut, F., Đurić, D., Đurić, U. et al. Application of geophysical and multispectral imagery data for predictive mapping of a complex geo-tectonic unit: a case study of the East Vardar Ophiolite Zone, North-Macedonia. Earth Sci Inform 17, 1625–1644 (2024). https://doi.org/10.1007/s12145-024-01243-4

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