WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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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.Conference Object Citation - WoS: 3Salihli Granitoid, Menderes Massif, Western Anatolia: a Sustainable Clean Energy Source for Mitigating Co2 Emissions(2022) Chandrasekharam, Dornadula; Ayzit, Tolga; Baba, AlperTurkey has a great opportunity to promote renewable energy, which is produced from high heat-generating granitoids using EGS (Enhanced Geothermal Systems) technology. Exploiting the energy from the radiogenic granitoid will help the country save about 32211 million kg of CO2 from gas-based electricity power plants. In addition to the hydrothermal energy sources, energy from EGS will make the country free from energy deficit and provide sustainable power, water, and food. In the present paper, we assess the power generation capacity of Salihli granitoid (SG), with an outcropping area of about 100 km2 located within the western Anatolian plateau, and describe the technology involved in harnessing the heat from these granitoids. The Anatolian Plateau is known for extension tectonics and is explained by the westward tectonic escape and subduction rollback processes. The most prominent structures of western Anatolia are E-W and ENE-WSW trending graben and horst controlled by low and high-angle oblique to dip-slip normal faults, exposing the Menderes Massif. Magmatic activity in western Anatolia is mainly related to episodic-two stage extensional regime, where the early phase is characterized mainly by calc-alkaline Early-Middle Miocene felsic lavas and pyroclastic and the latter by late Miocene-Quaternary rift-related alkaline basaltic volcanism. The plutonic activity started during 12 to 15 Ma represented by SG. The heat generation capacity of the SG varies from 5.5 to 6.7 (µW/m3), while the heat flow values over SG range from 68 to 107 HF (mW/m2). These values are much higher compared to the global average crustal values.