In our latest video, we break down the workflow to enhance the Thinned Fault Likelihood (TFL) using a two-step approach: Filtering and Computing RMS of TFL.
Here’s what you can expect:
- We start by launching automatic fault plane extraction tool
- Next we create fault skins derived from fault likelihood
- We apply filters based on size, dip, and strike
- Saving the refined output as Thinned Fault Likelihood
RMS Analysis of TFL
Now we address the visibility issue due to the ultra-thin nature of TFL (1 sample). We do this by leveraging the Volume Statistics attribute to compute the RMS of TFL around each sample position. This enhances the visibility on time-slices and horizons
Finally we look at a side-by-side comparison to illustrate the improvements and the precision achieved through this optimized workflow
Let us know what you think and share your insights after watching.