Fault Interpretation Workflow – Post 1/4: Pre-Processing & Filtering
Over the next four weeks, I’ll be sharing fault interpretation and extraction workflows in OpendTect.
Fault interpretation typically begins with Pre-processing, where we condition the seismic data to improve fault visibility and reduce noise. This is followed by visualizing fault attributes (See the slider for examples of filters and attributes).
OpendTect supports many Filters & Attributes, including:
🔹Dip-Steered Median Filter – noise reduction while preserving reflector continuity
🔹Edge-Preserving Smoother – TFL-guided smoothing that sharpens fault edges
🔹Dip-Steered Similarity / Polar Dip – highlight discontinuities and local dip variations
🔹Fault Enhancement Filter – increases fault contrast in both vertical and horizontal views
🔹Thinned Fault Likelihood (TFL) – high-resolution fault likelihood attribute producing sharp, well-defined fault images
🔹Machine Learning predicted Fault Likelihood - 2D and 3D pretrained models and workflows for tuning prediction results.
Pre-processing and fault visualization are critical steps guiding the user in the process of extracting fault planes, which can be done manually or automatically in OpendTect.
Next Thursday → 🔹Post 2: Manual Fault Plane Interpretation
🔹Post 3: Manual Fault Stick Interpretation followed by Grouping & Extraction of fault planes
🔹Post 4: Automatic Fault Plane Extraction