[SEG 2020] Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study
Channel: OLIVES at GATECH
Date: 2020-09-02

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 other surveys can provide much needed useful information for better generalization. We propose a learning scheme where we simultaneously train two network architectures, each on a different dataset. By placing a soft constraint on the weight similarity between the two networks, we make them learn from each other where useful for better generalization performance on their respective datasets. Using less than 3$\%$ of the available training data, we were able to achieve an average $r^{2}$ coefficient of 0.8399 on the acoustic impedance pseudologs of the SEAM dataset via joint learning with the Marmousi dataset.   

Citation Information
A. Mustafa, and G. AlRegib, "Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study," in Expanded Abstracts of the SEG Annual Meeting, Houston, TX, Oct. 11-16 2020. 

[Preprint]: https://arxiv.org/pdf/2006.15474.pdf

Related Works
1. A. Mustafa, M. Alfarraj, and G. AlRegib, "Joint Learning for Spatial Context-Based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness," Geophysics, submitted on Jun. 19 2020. (Under Review)

2. A. Mustafa, M. Alfarraj, and G. AlRegib, "Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation," in Expanded Abstracts of the SEG Annual Meeting, Houston, TX, Oct. 11-16 2020. [https://arxiv.org/pdf/2006.15472.pdf]

3. A. Mustafa, M. Alfarraj, and G. AlRegib, "Estimation of Acoustic Impedance From Seismic Data Using Temporal Convolutional Network," in Expanded Abstracts of the SEG Annual Meeting, Sep. 15-20 2019. [https://arxiv.org/abs/1906.02684]

4. M. Alfarraj, and G. AlRegib, "Semi-Supervised Sequence Modeling for Elastic Impedance Inversion," in Interpretation, Aug. 2019. [https://arxiv.org/abs/1908.07849]

5. M. Alfarraj and G. AlRegib, "Semi-Supervised Learning for Acoustic Impedance Inversion," in Expanded Abstracts of the SEG Annual Meeting, Sep. 15-20 2019. [https://arxiv.org/abs/1905.13412]

6. M. Alfarraj and G. AlRegib ., "Petrophysical Property Estimation From Seismic Data Using Recurrent Neural Networks," in Expanded Abstracts of the SEG Annual Meeting, Anaheim, CA, Oct. 14-19 2018. [https://arxiv.org/abs/1901.08623]

Affiliation
The authors are members of the Omni Lab of Intelligent Visual Engineering and Science (OLIVES) and the Center for Energy and Geo Processing (CeGP) at the Georgia Institute of Technology.

[Group Website]: https://ghassanalregib.info/
[Codes]: https://github.com/olivesgatech

#ReservoirCharacterization  #MachineLearning #JointLearningforGeneralization #SEG2020 #GeorgiaTech #NeuralNetworks #DeepLearning