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Explore advanced computational methods for solving inverse problems in geophysics through this seminar lecture delivered by Professor Stig Niklas Linde from Université de Lausanne. Discover how deep generative models can revolutionize geophysical inversion techniques, providing new approaches to extract subsurface information from geophysical measurements. Learn about the mathematical foundations and practical applications of these cutting-edge machine learning methods in geophysical data interpretation. Examine how generative models can handle uncertainty quantification and improve the reliability of geophysical inversions compared to traditional methods. Gain insights into the integration of deep learning architectures with geophysical forward modeling to solve complex inverse problems in earth sciences. This presentation is part of the "Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning" research programme at the Isaac Newton Institute for Mathematical Sciences.
