# Joint learning for seismic inversion: An acoustic impedance estimation case study

@article{Mustafa2020JointLF, title={Joint learning for seismic inversion: An acoustic impedance estimation case study}, author={Ahmad Mustafa and Ghassan AlRegib}, journal={Seg Technical Program Expanded Abstracts}, year={2020} }

Seismic inversion helps geophysicists build accurate reservoir models for exploration and production purposes. Deep learning-based seismic inversion works by training a neural network to learn a mapping from seismic data to rock properties using well log data as the labels. However, well logs are often very limited in number due to the high cost of drilling wells. Machine learning models can suffer overfitting and poor generalization if trained on limited data. In such cases, well log data from…

## 4 Citations

A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation

- Remote Sensing
- 2022

Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the…

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

- Computer Science, EngineeringArXiv
- 2021

By replacing 1-d convolutional neural network layers in deep learning structure with 2-d CNN layers and 2-D maxpooling layers, the prediction accuracy is improved and prediction uncertainty can also be estimated by embedding the network into a Bayesian inference framework.

A comparative study of transfer learning methodologies and causality for seismic inversion with temporal convolutional networks

- Computer ScienceFirst International Meeting for Applied Geoscience & Energy Expanded Abstracts
- 2021

Explainable seismic neural networks using learning statistics

- First International Meeting for Applied Geoscience & Energy Expanded Abstracts
- 2021

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