The use of machine learning in processing remote sensing data for mineral exploration скачать в хорошем качестве

The use of machine learning in processing remote sensing data for mineral exploration

Title: The use of machine learning in processing remote sensing data for mineral exploration Presenter: Dr. Ehsan Farahbakhsh Date: Wednesday 20th April 2022 Time: 1800-1900 Overview: The decline of the number of newly discovered mineral deposits and increase in demand for critical minerals in recent years has led exploration geologists to look for more efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits. Different types of remote sensing datasets, such as satellite and airborne data, make it possible to overcome common problems associated with mapping geological features. The rapid increase in the volume of remote sensing data obtained from different platforms has encouraged scientists to develop advanced, innovative, and robust data processing methodologies. Machine learning methods can help process a wide range of remote sensing datasets and determine the relationship between components such as the reflectance continuum and features of interest. These methods are robust in processing spectral and ground truth measurements against noise and uncertainties. In this presentation, I will provide a brief introduction to remote sensing data types and review the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data aiming at detecting various ore deposit types. I will also review our recent studies on combining remote sensing data and machine learning methods for mapping different geological features that are critical for providing mineral potential maps. Bio: Dr. Ehsan Farahbakhsh is a Research Associate in the EarthByte Group, School of Geosciences, University of Sydney. He holds a PhD degree in Mining Engineering - Mineral Exploration from Tehran Polytechnic. He has been involved in several projects as an exploration geologist or spatial data analyst for the exploration industry, primarily for providing prospectivity maps of various ore deposit types from regional to deposit scale. His research interests are multidimensional mineral prospectivity modeling, geological remote sensing, geostatistics, and the application of data science and UAVs in mineral exploration.