Machine learning in triassic paleogeography for fossil fuel extraction site discovery [Triyas paleocoğrafyasında makine öğrenimi ile fosil yakıt sondaj sahalarının keşfi]

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Erbay, Batıray and Şahin, Özlem (2024) Machine learning in triassic paleogeography for fossil fuel extraction site discovery [Triyas paleocoğrafyasında makine öğrenimi ile fosil yakıt sondaj sahalarının keşfi]. In: 32nd Signal Processing and Communications Applications Conference (SIU), Mersin, Turkiye

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Abstract

This study utilizes machine learning algorithms to determine the coordinates of potential fossil fuel reserves by associating existing fossil fuel fields with the geological structure of the Triassic period. A method has been developed to convert the current coordinates of the fields to those of the Triassic period, and the characteristics of the fields during the Triassic period have been extracted from two-dimensional maps using computer vision techniques. K-nearest neighbors, random forest, and XGBoost models were trained, and the best model predicted the presence of fossil fuels in the fields with 88% accuracy. The interdisciplinary approach employed demonstrates the effectiveness of machine learning in geo-spatial analysis and opens new avenues for improving the management of energy resources.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: artificial intelligence in energy sector; environmental data analysis; exploration techniques; fossil fuels; geospatial analysis; machine learning; paleogeography; petroleum exploration; tectonic plates
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Batıray Erbay
Date Deposited: 27 Aug 2024 10:59
Last Modified: 27 Aug 2024 10:59
URI: https://research.sabanciuniv.edu/id/eprint/49819

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