What is it about?
We present a probabilistic approach for seismic petrophysical inversion that includes the definition of a physics-informed neural network algorithm in a probabilistic setting, the use of an additional neural network for rock physics model hyperparameters estimation, and the implementation of Approximate Bayesian Computation to quantify the model uncertainty.
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Why is it important?
It can incorporate data and physics into neural networks for seismic petrophysical inversion.
Perspectives
A combination of neural networks, data, physics for seismic petrophysical inversion,
Peng Li
University of Wyoming
Read the Original
This page is a summary of: Probabilistic physics-informed neural network for seismic petrophysical inversion, Geophysics, January 2024, Society of Exploration Geophysicists,
DOI: 10.1190/geo2023-0214.1.
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