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Explore how AI tools can accelerate your seismic workflows
Schedule a 15-minute consultation to discuss how machine learning applies to your geologic settings and seismic interpretation challenges. Let’s get started!
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How Machine Learning Orchestration Works – Guided ThoughtFlows®
Machine Learning Stratigraphic Analysis
Machine Learning (ML) Stratigraphic Analysis uses the Self-Organizing Map (SOMs), an unsupervised machine learning process, to classify stratigraphic facies and their distributions. Applied at a single sample scale in Paradise, this process produces a detailed view of stratigraphy due to the stacking effect of classifying individual examples in attribute space. In addition to identifying thin beds, ML Stratigraphic Analysis also reveals potential fluid effects in seismic data. When used with the unique interactive 2D Colormap in Paradise, the distribution of one or more neural classes are calibrated with geology. The Machine Learning Geobody application then incorporates ML Stratigraphic Analysis results to produce geobodies and calculate volumetrics.
Automatic Fault Detection
Built on 3D CNN technology, the Deep Learning (DL) Fault Detection application is equipped with robust synthetic fault models, supporting a wide range of seismic data and geologic settings without needing user-provided fault examples for training. AI-based Fault Detection in Paradise uses supervised Deep Learning (DL) and unsupervised Machine Learning (ML) technologies to produce fault geobodies, which are easily converted to fault planes in an interpretation system.
Integrated Results
An excellent example of machine learning orchestration is the integration of ML-based Stratigraphic Analysis and automatic Fault Detection results. Together, guided ThoughtFlows® apply unsupervised SOM and supervised CNN, plus pre-and-post processing on fault volumes, to achieve results that are not through traditional interpretation methods and systems. Check out the papers below to learn more about these exciting breakthroughs and how they transform seismic interpretation.
Technical papers on machine learning orchestration using Paradise
Learn how geoscientists are using the Machine Learning Stratigraphic Analysis and Automatic Fault Detection in Paradise to generate integrated results.

Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells
An Enhanced Fault Detection Workflow Combining Machine Learning and Seismic Attributes Yields an Improved Fault Model for Caspian Sea Asset
“We have maintained for years that more can be gained from seismic data when it is analyzed using machine learning technology at a single sample resolution, and there is now an abundance of evidence to support this observation. We will continue to introduce off-the-shelf, fit-for-purpose applications to Paradise that have a strong return-on-investment for our customers.”
– Tom Smith, President & CEO of Geophysical Insights
Explore how AI Tools can Accelerate your Seismic Workflows
Schedule a 15-minute consultation to discuss how machine learning applies to your geologic settings and seismic interpretation challenges. Let’s get started!