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Thomas Hermans, Ghent University-Bayesian Evidential Learning: Get Away with Geophysical Inversion

Thursday, Nov 5, 2020 12:00 PM
Where: Passcode: 840912
Geophysics Department

Thomas Hermans

Department of Geology, Ghent University, Belgium

Bayesian Evidential Learning or How to (Almost) Get Away with Geophysical Inversion Issues

Imaging the subsurface of the Earth is the main objective of geophysicists. Deterministic inversions lack the ability to produce satisfactory quantification of uncertainty, whereas stochastic inversions are computationally (too) demanding. In this contribution, a new efficient stochastic method to interpret geophysical data avoiding direct inversion through machine learning is proposed. The methodology based on Bayesian Evidential Learning will be illustrated on different 1D, 2D and 3D examples from different data types (SNMR, ERT, surface wave seismic). Passcode: 840912

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