Learning from seismic data to characterize subsurface volumes

Alfarraj, Motaz A.

Title:

Learning from seismic data to characterize subsurface volumes

dc.contributor.advisor	AlRegib, Ghassan
dc.contributor.author	Alfarraj, Motaz A.
dc.contributor.committeeMember	McClellan, James H.
dc.contributor.committeeMember	Peng, Zhigang
dc.contributor.committeeMember	Anderson, David
dc.contributor.committeeMember	Zhang, Ying
dc.contributor.department	Electrical and Computer Engineering
dc.date.accessioned	2020-01-14T14:46:55Z
dc.date.available	2020-01-14T14:46:55Z
dc.date.created	2019-12
dc.date.issued	2019-11-11
dc.date.submitted	December 2019
dc.date.updated	2020-01-14T14:46:55Z
dc.description.abstract	The exponential growth of collected data from seismic surveys makes it impossible for interpreters to manually inspect, analyze and annotate all collected data. Deep learning has proved to be a potential mechanism to overcome big data problems in various computer vision tasks such as image classification and semantic segmentation. However, the applications of deep learning are limited in the field of subsurface volume characterization due to the limited availability of consistently-annotated seismic datasets. Obtaining annotations of seismic data is a labor-intensive process that requires field knowledge. Moreover, seismic interpreters rely on the few direct high-resolution measurements of the subsurface from well-logs and core data to confirm their interpretations. Different interpreters might arrive at different valid interpretations of the subsurface, all of which are in agreement with well-logs and core data. Therefore, to successfully utilize deep learning for subsurface characterization, one must address and circumvent the lack or shortage of consistent annotated data. In this dissertation, we introduce a learning-based physics-guided subsurface volume characterization framework that can learn from limited inconsistently-annotated data. The introduced framework integrates seismic data and the limited well-log data to characterize the subsurface at a higher-than-seismic resolution. The introduced framework takes into account the physics that governs seismic data to overcome noise and artifacts that are often present in the data. Integrating a physical model in deep-learning frameworks improves their generalization ability beyond the training data. Furthermore, the physical model enables deep networks to learn from unlabeled data, in addition to a few annotated examples, in a semi-supervised learning scheme. Applications of the introduced framework are not limited to subsurface volume characterization, it can be extended to other domains in which data represent a physical phenomenon and annotated data is limited.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/62310
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Deep learning
dc.subject	Semi-supervised learning
dc.subject	Sequence modeling
dc.subject	Subsurface characterization
dc.subject	Seismic inversion
dc.title	Learning from seismic data to characterize subsurface volumes
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	AlRegib, Ghassan
local.contributor.corporatename	School of Electrical and Computer Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	7942fed2-1bb6-41b8-80fd-4134f6c15d8f
relation.isOrgUnitOfPublication	5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral