Identifying mineral potentials related to geological structures using deep learning

Abstract

Artificial intelligence is increasingly being used as a powerful tool in various industries, including earth sciences. Geological structures play an undeniable role in the formation of mineral potentials. Investigating these structures relies on satellite imagery, together with expert interpretation, which can be a time-consuming process. Artificial intelligence can serve as a valuable tool to expedite this process and enhance the accuracy of mineral potential identification. This article presents a new model based on deep neural networks for identifying mineral potentials. The unique feature of the proposed method is the incorporation of morphological data alongside multispectral data to identify mineral potentials. To evaluate the effectiveness of the proposed method, advanced spaceborne thermal emission and reflection radiometer satellite images from a region in the southeast of Iran were utilized. The results demonstrate an improvement in the accuracy of the proposed method compared to similar approaches.

Keywords

Artificial intelligence / Deep learning / Deep neural network / Mineral potential identification / Machine learning

Funding

None.

References

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