Rafael Augusto Pires de Lima
Post-Doc, Department of Geography
University of Colorado Boulder
Feb 24, 2023, 3:35 PM - 5:00 PM
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Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine learning is becoming more popular in different fields of Earth Sciences, some concepts of convolutional neural networks may be vaguely understood by non-practitioners. In this presentation, I show some of the key concepts on the foundations of convolutional neural networks, some techniques that can help us understand them, and strategies that can be used to train these types of models. The objective of the presentation is not to focus on mathematical and implementation details, but to help build the intuition necessary to use and analyze the outputs of convolutional neural network models. The case studies include microfossil classification and core lithofacies classification, both using colored photographs. The final case study shows how convolutional neural networks can be used for sea ice segmentation using synthetic aperture radar.
Rafael has a bachelor in Geophysics from the University of São Paulo, a MSc. in Geophysics, a second MSc. in Data Science and Analytics, and a Ph.D. in Geophysics from the University of Oklahoma. His research interests include developing machine learning algorithms for different geospatial and geoscience applications. He also has experience with advanced image processing techniques for seismic attributes development, as well as data analysis for several geophysical methods. Rafael's current research is funded by a National Science Foundation grant focused on developing machine learning models for sea ice mapping using Synthetic Aperture Radar images and other remotely sensed data.