[SEG 2020]  Spatiotemporal Modeling of Seismic Images for Acoustic Impedance  Estimation
Channel: OLIVES at GATECH
Date: 2020-09-02

Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially. This utilizes information-relatedness in seismic traces in depth and spatial directions to make efficient rock property estimations.  We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally. Our results on the SEAM dataset demonstrate that, compared to the other architectures used in the study, the proposed workflow is able to achieve the best performance, with an average $r^{2}$ coefficient of 79.77\%.

Citation Information
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.

[Preprint]: https://arxiv.org/pdf/2006.15472.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, 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. [https://arxiv.org/abs/2006.15474]

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 #SpatiotemporalModeling #SEG2020 #GeorgiaTech #NeuralNetworks #DeepLearning