Comparison of Deep Learning
Fault
Interpretation From Seismic Data With Traditional and Attribute Based Techniques
Abstract
The growth in artificial intelligence, and Deep Learning in particular, now gives us the potential for a much faster and more accurate fault
delineation than has ever been possible before. By combining objective compute power with human cognition, Deep Learning algorithms now give us a much cleaner and more accurate
fault
interpretation.This paper presents a comparative study of the results of a traditional manual
fault
interpretation, an attribute driven
fault
interpretation, and the results from a Deep Learning algorithm. The results show that the Deep Learning algorithm significantly outperforms attribute analysis in terms of the accuracy of the
fault
detection and the almost complete lack of background noise. It also outperforms manual interpretation in terms of the speed of interpretation whilst maintaining accuracy. One of the most striking observations of this study is the ability of the Deep Learning network to perform well when the data quality is poor, its ability to differentiate faults from imaging related artefacts in the data, even when the seismic expression is similar. These results show the enormous power of Deep Learning to extract a more accurate
fault
interpretation in less time than either a manual interpretation or attribute driven analysis.
AAPG Datapages/Search and Discovery Article #90350 © 2019 AAPG Annual Convention and Exhibition, San Antonio, Texas, May 19-22, 2019