Mineralogical and Petrographical Features of Southeast Anatolian Metamorphic Complex (Pütürge Metamorphics, Eastern Taurides-Türkiye)

2024-02-22T09:26:09+08:00

Pütürge Metamorphics, located within the nappe zone in the Southeastern Anatolian Orogenic Belt (SAOB), crop out in large areas in the study area. Pütürge Metamorphics were examined in the Upper Metamorphic and Lower Metamorphic Unit. Lower Metamorphic Unit; It is represented by amphibolite, amphibole schist, biotite schist, granitic gneiss and augen gneises Upper Metamorphic Unit; with kyanite quartzite veins, muscovite schist, mica schist, garnet mica schist, garnet quartz mica schist, chloritoid chlorite schist and marbles. Pütürge Metamorphics start with augen gneisses at the base and are overlain by biotite schist-amphibole schist and granitic gneisses. At the top, there are kyanite quartzite veins, muscovite schist, mica schist, garnet mica schist, garnet quartz mica schist, chloritoid chlorite schist and marbles belonging to the Upper Metamorphic Unit. The main mineral assemblage of the Pütürge Metamorphics consists of quartz, alkali feldspar, plagioclase, biotite, muscovite, sericite, chlorite, garnet, tremolite actinolite, dental, chloritoid and calcite minerals in varying amounts.

Regional Mapping of Basement Lithologies Using Geospatial Data in Semi-Arid Regions: Techniques, Advancements and Applications

2024-02-22T09:40:56+08:00

Lithological mapping in semi-arid regions has witnessed a phase of transformation due to advancement in remote sensing technology. This has permitted a more comprehensive understanding of surface lithological units. This review explores the evolution of remote sensing mapping techniques and their diverse uses at semi-arid regions, underscoring the significance of the mapping procedure and the prospects. Remote sensing technology has been advancing with moderate to high resolution spaceborne and airborne sensors, unmanned aerial vehicle (UAV) technology and LiDAR (light detection and ranging). These have significantly enhanced capacity, accuracy and the scope of lithological mapping procedures. Especially, the advancement of machine learning and Artificial Intelligent (AI) in automated remote sensing data analysis has ignited more precise ways of identifying and classification of lithological units. Using hybrid remote sensing/machine learning mapping techniques has extended the horizon of geological studies where mineral exploration, water resource management, land use planning, environmental assessments, and risk mitigation are particularly considered. The maps derived provide deeper insights into accurate delineation of mineral deposits, identification of potential sources of water, and aiding those making informed decision making for land development and resource management. The importance of hybrid remote sensing/ machine learning techniques lies with the profound contributions made through geological history, resource exploration, environmental preservation, and risk management directed to fragile ecosystems such as semi-arid environments. The future of the hybrid methodologies holds promise for further advancements in integrating various data sources, exploitation of their contextual properties, refining AI algorithms for faster and more accurate analysis, and methodologies that are specific to environments. These evolving technologies and diverse applications present a trajectory targeted at more comprehensive utilization of geological resources and improvement of environmental stewardship even to fragile regions.

Advances in Geological and Geotechnical Engineering Research

Mineralogical and Petrographical Features of Southeast Anatolian Metamorphic Complex (Pütürge Metamorphics, Eastern Taurides-Türkiye)

Regional Mapping of Basement Lithologies Using Geospatial Data in Semi-Arid Regions: Techniques, Advancements and Applications