OpendTect’s attribute engine stands out due to its ability to compute attributes-from-attributes both on-the-fly and in batch-mode. Such capability lets users craft intricate chained attributes and filters. However, as these chains grow, they become more intricate, making it challenging to decipher their computation process.
To simplify this for users, OpendTect offers a data flow visualization for chained attributes, presented as a comprehensive graph. Powering this visualization is Graphviz, a reputable open-source graph visualization tool.
In the video, we dive into a predefined chained attribute included in OpendTect's Default Attribute sets - the Fault Enhancement Filter.
This filter assesses seismic data quality using the Similarity attribute:
In high-quality zones (noted by high Similarity), a Dip-Steered Median Filter with an extended step-out is employed.
Conversely, in lower-quality zones (indicated by low Similarity), a Diffusion Filter refines the Dip-Steered Median Filtered data with a limited step-out. The Diffusion Filter enhances seismic data quality by calculating the Similarity within a dip-steered radius around the main evaluation point. By replacing the main point's value with the optimal value found within this radius, the filter sharpens data edges, highlighting faults. For areas without edges, a Dip-Steered Median Filter is the preferred choice.
The decision on which filter to employ at each sample location is determined by a Mathematics attribute, which uses a logical expression and sets a threshold based on Similarity.
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