MATLAB accelerates the processing and analysis of both large-scale and real-time datasets including images and signals. It uses scalable and adaptive tools that integrate AI, high-performance computing, and signal and image processing capabilities. These features maximize data and image consumption and minimize turnaround times.
MATLAB is the integrated software platform for energy scientists and engineers to:
Analyze ultra-large datasets with parallel computing and GPU acceleration
Accelerate machine learning and deep learning to make data-driven decisions in real time
Automate anomaly detection and recognition with image processing and computer vision
Customize and deploy applications on IT infrastructure on-premises, in the cloud, or at the edge of the network
“High-performance computing with MATLAB enables us to process previously unanalyzed big data. We translate what we learn into an understanding of how human activities affect the health of ecosystems to inform responsible decisions about what humans do in the ocean and on land.”
Accelerate large-scale seismic modeling and migration using full-waveform inversion (FWI) and reverse time migration (RTM) algorithms, enhanced by high-performance computing in MATLAB.
Accelerate and automate seismic facies classification using wavelet signal decomposition. This method informs deep learning neural networks empowered by high-performance computing in MATLAB.
Discover how ARKCLS expedites subsurface data analysis by interconnecting digital interpretation systems and specialized software applications with MATLAB.
Discover how Chevron supports real-time decision-making during oil and gas production monitoring with reduced-order modeling, simulation, and machine learning in MATLAB.
Learn how Shell automates image predictive analytics for event detection in chemical plants using deep learning and high-performance computing in MATLAB.
Represent big data in MATLAB, adjust existing code to work efficiently with it, and scale up the analysis to take advantage of your own computing resources or a cloud.
Learn to use deep learning for signal processing tasks such as
labeling, classification, anomaly detection, signal analysis, and modifying networks
to solve custom signal processing problems.
Learn the analytics, signal processing, and machine learning techniques needed for predictive maintenance and condition monitoring workflows.
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