Tectonic and hydrogeochemical functions resulted in ophicalcite and listvenite formations thriving throughout tectonic lines in ultramafic rocks in Narman province of Erzurum and Alacakaya province of Elazig. These rocks are most commonly found in serpentinised ultramafites. Serpentine minerals thriving in Ophicalcites (lizardite, chrysotile) and talc, diopside and chromite accompanying these minerals indicate a peridotitic origin, the presence of fluid in the environment and a high reaction rate. In the mineral paragenesis of listvenites and ophicalcites determined by XRD method, quartz + dolomite, dolomite + calcite + quartz, dolomite + quartz + magnesite are mainly determined. In the serpentinised ultramafic rocks, serpentine + magnesite, magnesite + talc and finally magnesite + quartz formations show the change in the thermodynamical conditions (pH, reduction, permeability) and the activity of silica. Encircling such minerals as olivine and lizardite/chrysotile determined by mineralogical examinations, calcite, chlorite, iddingsite and hematite zones indicate progressing metamorphism conditions, the changes occurring in Mg-silicates with the effect of fluids rich in Ca and CO₂ and the formation of carbonate. Isotopic studies of δ¹³C/¹²C and δ¹⁸O/¹⁶O ratios of calcite from carbonates and breccia matrix are typical of hydrothermal deposits and serpentinite-carbonate metasomatites. As a result of microscopic examinations, and chemical and isotopic analyses, it can be seen that tectonism, carbonate activity thriving with the hydrothermal functions of medium or low temperature and the progressive metamorphism are effective in the formation of ophicalcites thriving in the ultramafics; the alterations of silicification-carbonatisation type thriving with the metasomatic functions in which the temperature is relatively high are active in the formation of listvenite.

Serpentine (Mg₃Si₂O₅(OH)₄), like quartz, dolomite and magnesite minerals, is a versatile mineral group characterized by silica and magnesium silicate contents with multiple polymorphic phases. Among the phases composed of antigorite, lizardite, and chrysotile, lizardite and chrysotile are the most prevalent phases in the serpentinites studied here. The formation process of serpentinites, which arise from the hydrothermal alteration of peridotites, influences the ratio of light rare earth elements (LREE) to heavy rare earth elements (HREE). In serpentinites, the ratio of light rare earth elements (LREE)/heavy rare earth elements (HREE) provides insights into formation conditions, geochemical evolution, and magmatic processes. The depletion of REE compositions in serpentinites indicates high melting extraction for fore-arc/mantle wedge serpentinites. The studied serpentinites show a depletion in REE concentrations compared to chondrite values, with HREE exhibiting a lesser degree of depletion compared to LREE. The high ΣLREE/ΣHREE ratios of the samples are between 0.16 and 4 ppm. While Ce shows a strong negative anomaly (0.1–12), Eu shows a weak positive anomaly (0.1–0.3). This indicates that fluid interacts significantly with rock during serpentinization, and highly incompatible elements (HIEs) gradually become involved in the serpentinization process. While high REE concentrations indicate mantle wedge serpentinites, REE levels are lower in mid-ocean ridge serpentinites. The enrichment of LREE in the analyzed samples reflects melt/rock interaction with depleted mantle and is consistent with rock–water interaction during serpentinization. The gradual increase in highly incompatible elements (HIEs) suggests that they result from fluid integration into the system and a subduction process. The large differential thermal analysis (DTA) peak at 810–830 °C is an important sign of dehydration, transformation reactions and thermal decomposition, and is compatible with H₂O phyllosilicates in the mineral structure losing water at this temperature. In SEM images, chrysotile, which has a fibrous structure, and lizardite, which has a flat appearance, transform into talc as a result of dehydration with increasing temperature. Therefore, the sudden temperature drop observed in DTA graphs is an indicator of crystal form transformation and CO₂ loss. In this study, the mineralogical and structural properties and the formation of serpentinites were examined for the first time using thermo-gravimetric analysis methods. In addition, the mineralogical and physical properties of serpentinites can be recommended for industrial use as additives in polymers or in the adsorption of organic pollutants. As a result, the high refractory nature of examined serpentine suggests that it is well-suited for applications involving high temperatures. This includes industries such as metallurgy and steel production, glass manufacturing, ceramic production, and the chemical industry.

The accurate identification of rock types is crucial for understanding geological structures and planning mining activities. Therefore, the precise labeling of rock types is a fundamental requirement for researchers and industry experts in these fields. This study aims to identify rock types by segmenting thin-section rock images using advanced deep learning models. Commonly used models such as DeepLabv3+, SegFormer, ConvNext, and Mask2Former were evaluated to compare the performance of different segmentation models. Additionally, an improved version of Mask2Former was analyzed to enhance its performance. Post-segmentation enhancements with SLIC super pixel and morphological operations further improved boundary delineation. An improved version of Mask2Former achieved a validation accuracy of 91.26% and a mean Intersection over Union (mIoU) of 82.59%, representing an improvement of more than 5% over the base Mask2Former. Another significant aspect of the study is the detailed analysis of other models using the relevant dataset. The Quartz Feldspar Plagioclase (QAP) diagram was utilized for mineral identification, achieving a mineral recognition accuracy of 85.7%. These results indicate the robustness of the proposed approach, which exceeds the accuracy and mIoU of comparable methods reported in the literature. This study significantly enhances the effectiveness of image processing techniques for rock type identification. Furthermore, the detailed comparison of different models provides valuable guidance for researchers in selecting the most suitable model.

In this study, the neutron and gamma radiation-shielding properties of serpentinites from the Guleman ophiolite complex were investigated, and results were evaluated in comparison with rare earth element (REE) content. The linear and mass attenuation coefficients (LAC and MAC), half-value layer (HVL), mean free path (MFP), and effective atomic numbers (Z_eff) of serpentinite samples were experimentally measured in the energy range of 80.99–383.85 keV. Theoretical MAC values were calculated. Additionally, fast neutron removal cross-sections, as well as thermal and fast neutron macroscopic cross-sections, were theoretically determined. The absorbed equivalent dose rates of serpentinite samples were also measured. The radiation protection efficiency (RPE) for gamma rays and neutrons were determined. It was observed that the presence of rare earth elements within serpentinite structure has a significant impact on thermal neutron cross-sections, while crystalline water content (LOI) plays an influential role in fast neutron cross-sections. Moreover, it has been observed that the concentration of gadolinium exerts a more substantial influence on the macroscopic cross-sections of thermal neutrons than on those of fast neutrons. The research results reveal the mineralogical, geochemical, morphological and radiation-shielding properties of serpentinite rocks contribute significantly to new visions for the use of this naturally occurring rock as a geological repository for nuclear waste or as a wall-covering material in radiotherapy centers and nuclear facilities instead of concrete.

The accurate classification of rocks is crucial for applications such as earthquake prediction, resource exploration, and geological analysis. Traditional methods rely on expert examination of thin-section images under a microscope, making the process time-consuming and prone to errors. Recent advancements in deep learning have emerged as a powerful tool for automated rock classification; however, distinguishing between similar rock types such as sedimentary, metamorphic, and magmatic rocks remains a challenge. This study proposes a novel hybrid convolutional neural network (CNN) approach that combines the strengths of VGG16 and EfficientNetV2 architectures for the classification of thin-section rock images. The model, developed using the Feature-Selected Hybrid Network (FSHNet), demonstrates significant improvements over individual models, achieving a 5% increase in accuracy compared to Efficient-NetV2B0 and a 9% increase compared to VGG16. By employing the ReliefF algorithm for feature selection and Support Vector Machines (SVMs) for classification, the model further reduces the dimensionality of the feature space, enhancing computational efficiency. The proposed model has been applied to two different rock datasets. The first dataset consists of 2634 images, categorized into sedimentary, metamorphic, and magmatic rock classes. Additionally, the approach was tested on a second dataset comprising petrographic microfacies images, demonstrating its effectiveness in multiclass geological structure classification. Validation on both datasets shows that the proposed method outperforms popular deep learning models and previous studies, achieving a 3% increase in accuracy. These results highlight that the proposed approach provides a robust and efficient solution for automated rock classification, offering significant advancements for geological research and real-world applications.