MACHINE LEARNING ORCHESTRATION
DETECT FAULTS AUTOMATICALLY
Rapid advances in Machine Learning (ML) are transforming seismic analysis. Geoscientists can accomplish the following quickly and effectively using these new tools:
- Identify thin beds and DHIs down to a single sample
- Run fault detection analysis in a few hours, not weeks
- Integrate fault and stratigraphic analysis volumes
Interpreters spend a lot of time identifying faults in seismic data by picking 2D lines. This critical process can now be automated with AI Fault Detection in Paradise, which uses deep learning and machine learning processes to generate fault volumes for fault interpretation. Check out the short video to the right to see results from AI Fault Detection, including for complex fault regimes or noisy seismic data. More examples of Fault Detection results are shown below. Select the technical papers below to learn more about fault interpretation and automatic Fault Detection.
How it 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.
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
“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