The size of the individual seismic surveys has increased over the last decade, along with the generation of megamerge and even larger, what some operators call "gigamerge" surveys. The number of useful attribute volumes has also increased, such that interpreters may need to integrate terabytes of data. During the past several years, various machine learning methods including unsupervised, supervised and deep learning have been developed to better cope with such large amounts of information. In this study we apply several unsupervised machine learning methods to a seismic data volume from the Barents Sea, on which we had previously interpreted shallow high-amplitude anomalies using traditional interactive interpretation workflows. Specifically, we apply K-means, principal component analysis, self-organizing mapping and generative topographic mapping to a suite of attributes and compare them to previously generated P-impedance, porosity and Vclay displays, and find that self-organized mapping and the generative topographic mapping provide additional information of interpretation interest.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 204B (Anaheim Convention Center)
Presentation Type: Oral