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Lunch & Learn | Automatic Identification of Fractures and Rock Mass Deformation in Clay Rocks Using Deep Learning
About This Webinar
Automatic Identification of Fractures and Rock Mass Deformation in Clay Rocks Using Deep Learning
Accurate and efficient identification of various discontinuities in clay rocks is crucial for monitoring and understanding rock mass behavior. In this webinar, we will present a state-of-the-art semantic segmentation approach for detecting and distinguishing fractures in borehole optical images using deep learning. The presentation will cover the training process of the model, key methodological insights, and practical applications. This approach offers a transformative tool for geoscientists and engineers working in rock mass analysis.
Who can view: People who attended or registered for the webinar only
Webinar Price: Free
Featured Presenters
Rushan Wang is a PhD candidate at the Swiss Federal Institute for Snow and Avalanche Research (SLF) and ETH Zurich. She holds a Master’s degree in Geomatics Engineering from ETH Zurich. Her research focuses on the intersection of Artificial Intelligence and Geoscience, with a particular emphasis on applying Computer Vision and Deep Learning techniques to automate fracture detection in clay rocks. Through her work, Rushan aims to bridge the gap between cutting-edge AI technologies and practical geoscientific challenges.
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