1887

Abstract

Advanced seismic interpretation most commonly rests on transforming original data representations by considering more or less numerous seismic attributes, which bear no explicit relation with geology. For this reason, they hardly allow fully solving problems such as reassembling sparse geological surface elements or specifying chronological or topological relationships between surfaces such as unconformity, on lap, interruption by fault. The present work intends to make further progress in geology-based interpretation of seismic data by using artificial intelligence tools based on cognitive vision. We propose a cognitive vision workflow for seismic interpretation based on a visual ontology and on three associated module dealing for data management, visual characterisation and geological correlation. An example of results is given showing the possibilities of the method for easily merging disconnected reflectors within one stratigraphical horizon taking into account simple geological criteria (amplitude, thickness, dip, vertical distance between reflectors).

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20147604
2008-06-09
2020-03-30
Loading full text...

Full text loading...

http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20147604
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error