Doctorate Sima Gorai: Research Scholar at CSIR-National Geophysical Research Instutute, Uppal Road, Hyderabad, Telangana, India

Dr. Sima Gorai is a dedicated geologist with a strong background in geochemistry, geological mapping, and the innovative use of machine learning techniques for mineral exploration. She completed her Ph.D. from the CSIR-National Geophysical Research Institute (NGRI), Hyderabad, where her research focused on the geochemistry and hydrothermal processes of the Zawar Pb-Zn deposit in Rajasthan, India. Dr. Gorai is passionate about applying advanced geochemical analysis methods, such as LA-ICPMS, SEM, and EDS, along with machine learning, to better understand mineral deposits and their formation processes. Her work significantly contributes to the evolving field of geoscience, particularly in mineral exploration and classification.

Online Profiles

ORCID Profile

Dr. Gorai’s academic and professional achievements are well-documented across several academic platforms, including her ORCID here, Google Scholar, and ResearchGate, where she regularly shares her publications, conference talks, and collaborations. Her profile showcases an evolving portfolio of groundbreaking research focused on integrating geochemistry and machine learning for enhanced understanding of geological systems. Dr. Gorai also maintains an active presence in the academic community, contributing to various international conferences and seminars.

Education

• Ph.D. in Geology and Geochemistry (2024), CSIR-NGRI, Hyderabad, India, focusing on the Zawar Pb-Zn deposit and machine learning applications in mineral classification.

• M.Sc. in Applied Geology (2016), Indian Institute of Technology, ISM Dhanbad, where she specialized in mineral exploration and geochemical analysis.

• B.Sc. in Geology (2011), University of Burdwan, West Bengal, laying the foundation for her expertise in geological studies.
  Dr. Gorai’s academic journey is marked by a commitment to blending geological theory with cutting-edge technological approaches to better interpret geological data.

Research Focus

Dr. Gorai’s research primarily delves into the geochemistry and mineralogy of the Zawar Pb-Zn deposit, using state-of-the-art geochemical analysis techniques, such as ICPMS and LA-ICPMS, to study trace elements and rare earth elements. Her work integrates machine learning algorithms to classify and interpret complex geochemical data, a cutting-edge approach in geological research. Furthermore, Dr. Gorai investigates the hydrothermal alteration zones and the role of acidic brines in ore deposit formation, with particular emphasis on the Zawar deposit in the Aravalli Supergroup. Her innovative work in combining remote sensing data with geological mapping tools like ArcGIS and ERDAS is advancing the field of mineral exploration and contributing to more accurate deposit modeling.

Experience

Dr. Gorai’s research experience spans over 5 years at CSIR-NGRI, where she focused on the Zawar Pb-Zn deposits, applying her knowledge of geochemistry, remote sensing, and machine learning techniques. Her expertise includes conducting in-depth petrographic studies using SEM and EDS, as well as applying ICPMS and LA-ICPMS for trace and rare earth element analysis. In addition to her research work, Dr. Gorai served as a Project Associate for 8 months, focusing on applying machine learning algorithms to analyze geological data, specifically to trace pyrite origin in mineral deposits. She has collaborated extensively on several research papers and conference abstracts, showcasing her skills in geospatial analysis and predictive modeling for ore deposit classification. Dr. Gorai’s work continues to shape the direction of research in mineralogy and geochemistry, particularly within the context of modern machine learning applications.

Research Timeline

• 2016-2020: Dr. Gorai began her doctoral research at CSIR-NGRI, focusing on the detailed geochemical and mineralogical study of the Zawar Pb-Zn deposit. She integrated machine learning techniques to classify and analyze trace elements using LA-ICPMS and other advanced tools.

• 2020-2024: Post-doctoral work as a Project Associate, continuing research on the use of machine learning in geochemical data classification. She conducted studies on the origin of pyrite in the Zawar deposit and applied remote sensing for geological mapping.

• Ongoing: Dr. Gorai is currently involved in multiple international collaborations, focusing on the integration of AI and remote sensing data for predicting ore deposit locations and understanding hydrothermal alteration patterns. She is also working on a significant paper regarding the role of acidic brines in ore deposit genesis.

Awards & Honors

• CSIR-UGC NET (JRF & Lectureship): Rank 77, June 2017, recognizing her academic excellence in the field of geological sciences.

• GATE 2014: Scored 318, qualifying in the geological sciences exam.

• Goldschmidt Conference 2021 & 2022: Invited to present her research findings on geochemical processes in Pb-Zn deposits.

• International Research Grants: Dr. Gorai received funding for various research projects focused on the integration of machine learning into geochemical exploration methods.
  These awards and recognitions underscore Dr. Gorai’s contribution to the field of geology and geochemistry.

Top-Noted Publication

• Gorai, S., et al. (2024). “Integration of Machine Learning with in-situ LA-ICP-MS Trace Element Analysis: Multi-Classification Approach Reveals the Hydrothermal Origin of Sphalerite in the Zawar Zn-Pb Deposit, Rajasthan, India.” Journal of Geological Society of India (Accepted). This publication stands out for its innovative approach to combining machine learning techniques with geochemical analysis, revealing new insights into the hydrothermal origins of sphalerite in one of India’s key mineral deposits.

• Gorai, S., et al. (2024). “Integrated Remote Sensing and Petrographic Guide to Delineate the Hydrothermal Alteration Zones Along the Phyllites of the Main Zawar Fold, Rajasthan, India.” Journal of the Indian Society of Remote Sensing, 1-14. This paper integrates remote sensing data with petrographic studies to improve understanding of hydrothermal alteration zones in the Zawar deposit.