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Abstract

Summary

By measuring the parameter uncertainty of the deep neural net. weights and biases by adding an additional Monte Carlo Dropout layer, the proposed Monte Carlo Dropout based Deep Neural Net. quantifies the uncertainty in the process of facies classification using seismic data and outputs a better classification than the traditional Deep Learning model.

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/content/papers/10.3997/2214-4609.202271011
2022-05-31
2024-04-29

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References

  1. [1]GalY. & GhahramaniZ., 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, Proceedings of the 33rd International Conference on Machine Learning
     [Google Scholar]
  2. [2]GeronA., 2019. 2nd edition, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202271011
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A Monte Carlo Dropout Based Deep Learning Model to Quantify Uncertainty in Facies Classification
Conference Proceedings 2022, 1 (2022); https://doi.org/10.3997/2214-4609.202271011
/content/papers/10.3997/2214-4609.202271011
/content/papers/10.3997/2214-4609.202271011
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