DETECT FAULTS AUTOMATICALLY

DETECT FAULTS AUTOMATICALLY

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.

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.

Technical papers on fault detection using Paradise

Learn how geoscientists are using the AI Fault Detection in Paradise to produce a refined fault volume, ready to import into an interpretation system.

Key Features:

Paradise AI Seismic Fault Detection Result

Fault Detection

Detect fault automatically and provide a volumetric prediction of faults

Pre-trained Models

Equip with general pre-trained CNN models (conservative and aggressive)

Deep Leaning Technology

Develop structural and stratigraphic seismic interpretation based on convolutional neural network (CNN) technology.

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

Paradise seismic interpretation software monitor

Equipped with general pre-trained deep learning engines (conservative and aggressive), the new Deep Learning (DL) Fault Detection application in Paradise enables the application to a wide range of seismic data without the need of user-provided fault examples for training. This tool dramatically reduces the time to identify faults in a volume, which allows interpreters to spend more time on the implications of seismic data.

  • Improve fault continuity and resolution
  • Increase accuracy in fault detection
  • Reduce sensitivity to artifacts and noise
  • Produce fewer false positives
  • Generate a more accurate and complete understanding of the subsurface

KEY TECHNOLOGIES IN PARADISE AI WORKBENCH:

Seismic Facies Classification

Deep learning Seismic Facies Classification enables the identification of structural and stratigraphic facies patterns using Convolutional Neural Network (CNN) as an image recognition process.

Automatic Fault Detection

Equipped with general pre-trained deep learning engines (conservative and aggressive), Fault Detection in Paradise can be applied to a wide range of seismic data without the need of user-provided fault examples for training.

Multi-Attribute Classification

Applies machine learning to reveal thin beds below conventional tuning thickness.

Attribute Generation

The Paradise AI workbench has a world-class library of instantaneous, geometric, and spectral decomposition attributes. Over 100 attribute can be generated.

Geobody Detection

Uses machine learning to identify potential reservoirs and estimate reserves.

Attribute Selection

Principal Component Analysis (PCA), a guided ThoughtFlow® process, is to identify attributes that are contributing the most energy to a region.

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