Joint Learning with Spatiotemporal Models for Reservoir Characterization | CSIP Seminar Talk

This is a video of the seminar talk I delivered at the Center for Signal and Information Processing (CSIP), Georgia Institute of Technology. It is based on our work titled "Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness" recently accepted for publication to the prestigious Geophysics Journal. Abstract: Seismic inversion plays a very useful role in detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geological variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We propose a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple datasets are simultaneously used for training, sharing beneficial information among each other. This results in the improvement in generalization performance on all datasets. We present a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 datasets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. Citation: A. Mustafa, M. Alfarraj, and G. AlRegib, 2021, "Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness," in Geophysics, vol. 86, no. 4. Preprint: https://arxiv.org/pdf/2104.02750.pdf GitHub: https://github.com/olivesgatech/Geoph... Related Works: 1. A. Mustafa and G. AlRegib, 2020, "Joint learning for seismic inversion: An acoustic impedance estimation case study," SEG Technical Program Expanded Abstracts : 1686-1690. 2. A. Mustafa, M. Alfarraj, and G. AlRegib, 2020, "Spatiotemporal modeling of seismic images for acoustic impedance estimation," SEG Technical Program Expanded Abstracts : 1735-1739. Author Bio: I am Ahmad, a PhD student in ECE at the Georgia Institute of Technology. My research interests lie in solving data-constrained problems in Deep Learning and Signal Processing. In particular, I work on applying novel DL concepts to various application areas in computational seismic interpretation. I work under the supervision of Professor Ghassan AlRegib at the Omni Lab for Intelligent Visual Engineering and Science (OLIVES). For more information about our work, please visit our website at https://ghassanalregib.info/ and our GitHub page at https://github.com/olivesgatech.