# Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training

arXiv: https://arxiv.org/abs/2111.10596 · HTML: https://mlsanity.com/paper/2111.10596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](https://mlsanity.com/paper/1512.05287/) (2015)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Semi-supervised Sequence Modeling for Elastic Impedance Inversion](https://mlsanity.com/paper/1908.07849/) (2019)
- [A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification](https://mlsanity.com/paper/2001.04567/) (2020)
- [Semi-supervised Learning for Acoustic Impedance Inversion](https://mlsanity.com/paper/1905.13412/) (2019)
- [Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation](https://mlsanity.com/paper/2006.15472/) (2020)
- [Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study](https://mlsanity.com/paper/2006.15474/) (2020)
- [Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks](https://mlsanity.com/paper/1901.08623/) (2019)

## Built on by (most cited first, 1 of 1)

- [ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion](https://mlsanity.com/paper/2302.06441/) (2023)

## Abstract (arXiv)

Seismic impedance inversion can be performed with a semi-supervised learning algorithm, which only needs a few logs as labels and is less likely to get overfitted. However, classical semi-supervised learning algorithm usually leads to artifacts on the predicted impedance image. In this artical, we improve the semi-supervised learning from two aspects. First, by replacing 1-d convolutional neural network (CNN) layers in deep learning structure with 2-d CNN layers and 2-d maxpooling layers, the prediction accuracy is improved. Second, prediction uncertainty can also be estimated by embedding the network into a Bayesian inference framework. Local reparameterization trick is used during forward propagation of the network to reduce sampling cost. Tests with Marmousi2 model and SEAM model validate the feasibility of the proposed strategy.