Channel - KAUST Virtual Workshop Intelligent illumination of the Earth
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that has shown recently incredible potential in solving problems efficiently and providing a measure of certainty to the solution. It has experienced rapid growth in the last decade across diverse industries, including communications, financial services, security, transportation, and others. A combination of large-scale data, a new array of Machine Learning tools, robust mathematical foundations, big advances in computer hardware and software led to the development of Machine Learning applications which produced dramatic results, enabling new opportunities. Among these opportunities are finding solutions to the many Earth discovery problems. From seismic to well, gravity and magnetics, and all other measurements of the Earth physical properties, the geosciences community is now realizing the potential of machine learning in building a realistic model of the Earth from Oil & Gas exploration and production, to geotechnical and environmental applications. This first of its kind of workshop in KAUST (combining Machine Learning with Earth sciences) aims to highlight the challenges, opportunities, and trends related to the adoption of Machine Learning in geosciences research and industrial workflows. Researchers from academia, geological bodies, Oil & Gas, and technology companies will present applications and case studies, promote discussion, and propose practical solutions to take greater advantage of Machine Learning methods.
7/1/2021 1:15:34 PM

Channel Videos

Active learning to reduce human effort needed for seismic data processing parametrization
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6/21/2021 1:50:00 PM
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Applicability of ML in Capturing the First Principles of Fluid Flow in Porous Media
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6/20/2021 12:30:00 PM
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GAN-based 3D Carbonate Digital Rock Reconstruction
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6/21/2021 12:40:00 PM
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Is it time to swish? Comparing activation functions in solving the Helmholtz equation using physics-informed neural networks
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6/22/2021 11:30:00 AM
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Train Deep Learning Models Using Outcrops Geological Images Datasets
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6/22/2021 11:10:00 AM
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Using machine learning for petrographic thin section analysis: A case study of Late Jurassic Upper Jubayla Formation carbonates, Saudi Arabia
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6/21/2021 12:30:00 PM
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AI-based Modeling and Optimization for Field Operations
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6/22/2021 2:30:00 PM
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Applying Machine Learning to Satellite Imagery for Tracking Deforestation
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6/22/2021 11:50:00 AM
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Artificially intelligent waveform inversion: physics-guided neural network functions
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6/22/2021 1:30:00 PM
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Automating subsurface property modeling through semi-supervised learning
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6/20/2021 1:30:00 PM
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Bayesian Deep DCA: A New Approach for Well Oil Production Modeling and Forecasting
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6/21/2021 2:20:00 PM
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Computational Methods for Seismic Inversion: Data-driven VS Physics-driven or Combined?
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6/22/2021 12:50:00 PM
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Deep learning and inference for seismic inverse problems
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6/23/2021 2:00:00 PM
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Deep learning for interpreting geologic structures
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6/21/2021 1:30:00 PM
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Deep learning geophysics: applications on data processing and inversion
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6/23/2021 11:00:00 AM
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Deep learning in EM inversion with prior and posterior shaping
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6/21/2021 2:30:00 PM
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ExaGeoStat: Exploiting Data Sparsity in Earth System Models
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6/21/2021 12:10:00 PM
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Fast and reliability-aware seismic imaging with conditional normalizing flows
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6/22/2021 1:10:00 PM
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Fracture recognition with U-net and pixel-based automatic fracture detection
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6/22/2021 11:00:00 AM
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Inversion and imaging with deepfake data
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6/23/2021 1:20:00 PM
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KAUST Supercomputing Services for HPC and AI
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6/20/2021 2:30:00 PM
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Machine learning applied to seismic reflection and well-log data for reservoir characterization
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6/21/2021 2:10:00 PM
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Machine learning to replace or to augment the physics?
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6/22/2021 12:30:00 PM
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Metrics and Transfer Learning for Seismic Inversion
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6/23/2021 1:40:00 PM
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Neural network-based CO2 interpretation from 4D Sleipner seismic images
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6/21/2021 12:50:00 PM
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NVIDIA platform for AI and HPC, and applications to geosciences
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6/20/2021 1:50:00 PM
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Open Seismic: A Toolbox for Seismic Interpretation
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6/20/2021 2:10:00 PM
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Opening notes
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6/20/2021 11:10:00 AM
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Petroleum Data Analytics; Engineering Application of Artificial Intelligence & Machine Learning in the Petroleum Industry
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6/22/2021 3:00:00 PM
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Physics-informed neural network for high-frequency wavefield representation
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7/21/2021 12:00:00 PM
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Preconditioning seismic processing problems with neural networks
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6/23/2021 12:00:00 PM
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Scattered wavefield reconstruction and velocity inversion based on physics-informed neural networks
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6/22/2021 11:40:00 AM
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Seismic tomography using physics-informed neural networks
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6/23/2021 11:40:00 AM
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Session I Q&A
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6/20/2021 12:50:00 PM
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Session II Q&A
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6/20/2021 2:50:00 PM
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Session III Panel discussion : "The future of ML in Geosciences and Engineering"
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6/21/2021 11:00:00 AM
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Session IV Q&A
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6/21/2021 1:00:00 PM
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Session IX Q&A
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6/23/2021 12:20:00 PM
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Session V Q&A
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6/21/2021 2:50:00 PM
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Session VI Q&A
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6/22/2021 12:00:00 PM
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Session VII Q&A
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6/22/2021 1:50:00 PM
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Session X Q&A
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6/23/2021 2:20:00 PM
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Talk 8.1 Q&A
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6/22/2021 2:50:00 PM
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Talk 8.2 Q&A
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6/22/2021 3:20:00 PM
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Towards Complete Machine-Learning-Based Earthquake Monitoring Workflows
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6/23/2021 1:00:00 PM
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Unsupervised deep learning inversion of seismic data based on physical-driven
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6/22/2021 11:20:00 AM
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Welcome presentation
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6/20/2021 11:00:00 AM
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Welcome presentation - Part 2
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6/20/2021 11:20:00 AM
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What Can Algorithms Learn from Data & Knowledge to Make Better Reservoir Predictions?
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6/20/2021 11:30:00 AM
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Workshop Closing
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6/23/2021 3:00:00 PM
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