Best Paper Award
| Abdelnasser Refaey Elwardany |
| Affiliation |
Istanbul Technical University |
| Country |
Turkey |
| Scopus ID |
57193522232 |
| Documents |
29 |
| Citations |
301 |
| h-index |
9 |
| Subject Area |
Earth and Planetary Sciences |
| Event |
Best Paper Awards |
| ORCID |
0000-0001-5994-7088 |
Abdelnasser Refaey Elwardany
Abdelnasser Refaey Elwardany of Istanbul Technical University, Turkey, is recognized in connection with the Best Paper Award for research in Earth and Planetary Sciences. This academic profile highlights the researcher’s scholarly record and the featured publication titled “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt.”
The featured research brings together remote sensing, machine learning, geological interpretation, and mineral exploration to investigate gold-mineralized alteration zones in the Fatira Mine Area of Egypt. The work represents an interdisciplinary approach to geological mapping and the identification of alteration patterns associated with mineralization.
Abstract
This article recognizes Abdelnasser Refaey Elwardany in connection with the Best Paper Award for research addressing the application of remote sensing and machine learning to the mapping of gold-mineralized alteration zones. The featured study, “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt,” examines the use of satellite-derived information and computational techniques to support geological interpretation and mineral exploration. The research is situated within the broader field of Earth and Planetary Sciences and demonstrates an interdisciplinary approach to mineral-resource investigation.
Keywords
Remote Sensing, Machine Learning, Gold Mineralization, Alteration Zones, Mineral Exploration, Fatira Mine, Egypt, Geological Mapping, Earth and Planetary Sciences, Satellite Imagery.
Introduction
Remote sensing has become an important tool in geological investigations because satellite observations can provide spatially extensive information for the identification and interpretation of lithological and alteration features. In mineral exploration, remotely sensed spectral information can complement field observations and conventional geological mapping by helping researchers identify spatial patterns that may be associated with mineralized systems.
Research Profile
Abdelnasser Refaey Elwardany is affiliated with Istanbul Technical University in Turkey and is associated with the subject area of Earth and Planetary Sciences. The supplied academic profile records 29 documents, 301 citations, and an h-index of 9. These indicators provide a quantitative overview of the researcher’s indexed scholarly output and citation activity.
The research profile is particularly relevant to geological and planetary sciences because the featured work addresses mineralized alteration, remote sensing, and machine-learning-assisted geological interpretation. The combination of earth-science knowledge with computational analysis reflects the interdisciplinary nature of contemporary mineral exploration research.
Research Contributions
The featured study contributes to mineral exploration research by examining how remote-sensing data and machine-learning techniques can be combined to identify gold-mineralized alteration zones. The approach provides a framework for extracting geological information from satellite observations and using computational methods to support the interpretation of alteration patterns.
Publications
The featured publication associated with this recognition is “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt.” The publication addresses the application of remote sensing and machine-learning methodologies to geological mapping and mineral exploration in the Fatira Mine Area.
| Publication Detail |
Information |
| Paper Title |
Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt |
| Research Area |
Earth and Planetary Sciences |
| Primary Methods |
Remote Sensing and Machine Learning |
| Study Location |
Fatira Mine Area, Egypt |
| DOI |
https://doi.org/10.1111/1755-6724.15333 |
Research Impact
The research has potential significance for geological and mineral exploration because it demonstrates the application of computational techniques to remotely sensed geological information. Mapping alteration zones is an important component of mineral exploration, and remote sensing can provide a means of examining large areas before more detailed field-based investigations are undertaken.
The interdisciplinary character of the work also contributes to the broader development of data-driven Earth sciences. By connecting satellite observations with machine-learning analysis, research of this type can support more systematic approaches to geological interpretation and exploration targeting.
Award Suitability
The Best Paper Award recognizes research that demonstrates scholarly quality, methodological relevance, originality, and meaningful contribution to its field. The featured publication is relevant to these considerations because it addresses a defined geological problem and applies remote sensing and machine-learning techniques to the investigation of gold-mineralized alteration zones.
Conclusion
Abdelnasser Refaey Elwardany’s academic recognition profile highlights research in Earth and Planetary Sciences with a focus on remote sensing, machine learning, geological mapping, and mineral exploration. The featured paper, “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt,” represents an interdisciplinary approach to investigating alteration zones associated with gold mineralization.
The combination of earth-science investigation and computational analysis provides a relevant foundation for research in modern mineral exploration. Together with the supplied publication and bibliometric profile, these characteristics provide an academic basis for consideration within a Best Paper Award framework.
External Links
References
- Elwardany, A. R. et al. Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica – English Edition. DOI: https://doi.org/10.1111/1755-6724.15333 ↩
- Scopus Author Profile. Abdelnasser Refaey Elwardany, Author ID 57193522232. Scopus Author Profile ↩
- ORCID. Research Profile of Abdelnasser Refaey Elwardany. ORCID iD: 0000-0001-5994-7088. https://orcid.org/0000-0001-5994-7088 ↩