Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Development and Validation of Regression Model via Machine Learning to Estimate Thermal Conductivity and Heat Flow Using Igneous Rocks from the Dikili-Bergama Geothermal Region, Western Anatolia(Pergamon-Elsevier Science Ltd, 2026) Ayzit, Tolga; Sahin, Onur Gungor; Erol, Selcuk; Baba, AlperThermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling.Article Citation - WoS: 1Citation - Scopus: 1Petrogenesis and Comprehensive Thermal Assessment of the Dikili-Bergama Region, Western Anatolia(Pergamon-elsevier Science Ltd, 2025) Ayzit, Tolga; Erol, Selcuk; Baba, AlperVarious methods are available to evaluate the thermal properties and energy potential of geothermal fields. The heat flow method is crucial for thermal modeling and understanding geological evolution. It helps to assess the impact of geological formations on various processes, including hydrocarbon generation and structural modeling. This study focuses on the Dikili-Bergama geothermal region and presents heat flow trends based on thermal modeling. The analysis of volcanic rock petrogenesis data and a thermal model are presented based on data from deep and shallow boreholes. The geothermal gradient is found to vary between 66.28 degrees C km-1 and 121.68 degrees C km-1, according to the interpolated data. Additionally, the study investigates the geochemical and lithological properties of magmatic rocks in the Dikili-Bergama region. The Kozak pluton group's has been measured to have radioactive heat production of up to 7.4 mu Wm-3. Thermal conductivity properties and correlations, along with heat flow assessment, contribute to the understanding of geothermal potential. The mean dry thermal conductivity of the rocks in the study area is 2.33 Wm-1K-1. The data for the terrestrial heat flow and the radioactive heat flow values are up to 200 mWm-2. The integration of 3D geological models and thermal models has highlighted the south western area of the study as a promising location for unconventional geothermal operations.