Prof. Upendra K Singh | Deep Machine Learning | Best Researcher Award
Professor at the Department of Applied Geophysics, IIT(ISM), Dhanbad, India
ย Prof. Upendra K. Singh, a distinguished geophysicist at IIT(ISM) Dhanbad, specializes in cutting-edge geophysical optimization and inversion techniques. With a Ph.D. focused on DC resistivity data using Neural Networks, his research spans seismic parameter estimation, innovative global optimization, and joint inversion methodologies. Presented at international conferences, his work leverages metaheuristic algorithms like Particle Swarm Optimization and Gibbโs Sampler. Prof. Singh’s multifaceted expertise covers sensitivity analysis, uncertainty assessment, and the application of advanced techniques in geophysical exploration. A prominent figure in the field, he continues to contribute significantly to the evolving landscape of geophysics. ย
Professional Profiles:
ย Education & Position:
ย Prof. Upendra K. Singh, a Geophysics expert, currently serves as a Professor at the Department of Applied Geophysics, IIT(ISM), Dhanbad, India. His journey began as a Junior Research Fellow, progressing to Scientist ‘B’ at the National Centre for Antarctic & Ocean Research.ย ย
ย Awards & Recognitions:
Acknowledged for his outstanding contributions, Prof. Singh participated in the 24th Indian Antarctic Expedition and received the Van Weelden Award for a research paper in Geophysical Prospecting.
ย Academic & Professional Journey:
From a Senior Lecturer at Kurukshetra University to his current role as a Professor at IIT(ISM), Dhanbad, Prof. Singh has enriched his academic and professional profile. His expertise spans exploration geophysics, marine geophysics, and legal continental shelf delineation.
ย Research Work & Technical Reports:
Prof. Singh’s research involves developing and applying Neural Networks for the analysis and interpretation of DC resistivity data. He has contributed to diverse projects, such as deep resistivity sounding studies, integrated geophysical studies, and legal continental shelf projects.
Research Focus:
His research centers on geophysical exploration methods, specializing in gravity, magnetic, electrical, and electromagnetic techniques. Prof. Singh is also involved in global optimization studies.
Prof. Upendra K. Singh’s research revolves around innovative approaches in geophysical optimization and inversion techniques. His work, presented at various international conferences, delves into cutting-edge methods such as Time Varying Inertia Weight-Particle Swarm Optimization, Two-Dimensional inversion, and Joint Inversion of MT and DC Resistivity. With a strong emphasis on metaheuristic algorithms like Gibbโs Sampler and Particle Swarm Optimization, Prof. Singh’s expertise lies in resolving seismic and resistivity data. His contributions span diverse areas, from seismic parameter estimation to uncertainty analysis and sensitivity assessment, showcasing a multifaceted research focus in the field of geophysics.
ย Marine Geophysics:
With extensive experience in marine data acquisition, processing, and analysis, Prof. Singh has actively contributed to delineating the legal continental shelf around the East Coast, Andaman-Nicobar, and West Coast.
ย Professional Associations:
A dedicated member of professional organizations like IEEE, Prof. Singh continues to leave a lasting impact on the field of geophysics through his research, teaching, and international collaborations.
Peer Reviewer & Academic Engagements:
Prof. Upendra K. Singh’s, citation metrics and indices from Google Scholar are as follows:
Citations: 1909 (All), 1382 (Since 2019)
h-index: 25 (All), 20 (Since 2019)
i10-index: 67 (All), 55 (Since 2019)
Publications (TOP NOTES)
Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study, publication date: 2024.
Assessment of Reservoir Heterogeneities and Hydrocarbon Potential Zones Using Wavelet-Based Fractal and Multifractal Analysis of Geophysical Logs of Cambay Basin, India, publication date: 2023.