Title:
Computational seismic interpretation using attention models, texture dissimilarity, and learning
Computational seismic interpretation using attention models, texture dissimilarity, and learning
| dc.contributor.advisor | AlRegib, Ghassan | |
| dc.contributor.author | Shafiq, Muhammad Amir | |
| dc.contributor.committeeMember | McClellan, James H. | |
| dc.contributor.committeeMember | Anderson, David V. | |
| dc.contributor.committeeMember | Stuber, Gordon L. | |
| dc.contributor.committeeMember | Peng, Zhigang | |
| dc.contributor.department | Electrical and Computer Engineering | |
| dc.date.accessioned | 2018-05-31T18:15:03Z | |
| dc.date.available | 2018-05-31T18:15:03Z | |
| dc.date.created | 2018-05 | |
| dc.date.issued | 2018-04-06 | |
| dc.date.submitted | May 2018 | |
| dc.date.updated | 2018-05-31T18:15:03Z | |
| dc.description.abstract | The exploration of oil and gas is a vital part of today's increasing power demands to meet the energy we need to power our homes, businesses, and transportation. Oil and gas explorers use seismic surveys, both onshore and offshore, to produce detailed images of the various rock types, layers, and their locations beneath the Earth's subsurface. The acquired data undergo a series of processing steps, which require powerful computing hardware, sophisticated software, and specialized manpower. To extract useful information from seismic data, interpreters manually delineate important geological structures, which contain hints about petroleum and gas reservoirs such as salt domes, faults, channels, fractures, and horizons. These structures typically span over several square kilometers and are delineated based on correlation, changes in illumination, intensity, contrast, and texture of seismic data. There are limited tools available for automatic detection and manual interpretation is becoming extremely time consuming and labor intensive. In this dissertation, we propose novel seismic attributes based on texture dissimilarity, visual-attention theory, the modeling of human visual system, and machine learning to quantify changes and highlight geological features in a three-dimensional space. To automate the process of seismic interpretation, we develop interpreter-assisted, fully-, and semi-automated workflows that are interactive and easy-to-use for the delineation of important geological structures within seismic volumes. Experimental results on real and synthetic datasets show that our proposed algorithms outperform the state-of-the-art methods for seismic interpretation. In a nutshell, this dissertation introduces novel seismic attributes and automated, interactive, and interpreter-assisted workflows, which have a very promising future in effective seismic interpretation. The proposed research is computationally inexpensive and is expected to not only reduce the time for seismic interpretation but also become a handy tool in the interpreter's toolbox for detecting and delineating important geological structures. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/59889 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Seismic interpretation | |
| dc.subject | Automation | |
| dc.subject | Gradient of texture | |
| dc.subject | Visual saliency | |
| dc.subject | Salsi | |
| dc.subject | SeisSal | |
| dc.subject | Unsupervised learning | |
| dc.subject | Autoencoder | |
| dc.subject | 3D FFT | |
| dc.subject | Multispectral projections | |
| dc.subject | Directional center-surround | |
| dc.subject | Feature maps | |
| dc.subject | Human visual system | |
| dc.subject | Texture dissimilarity | |
| dc.subject | F3 block | |
| dc.subject | Netherlands | |
| dc.subject | North Sea | |
| dc.subject | SEAM | |
| dc.subject | Great South Basin | |
| dc.subject | New Zealand | |
| dc.subject | Stratton | |
| dc.subject | Listric faults | |
| dc.subject | Texas Gulf Coast | |
| dc.subject | Sparse classification | |
| dc.title | Computational seismic interpretation using attention models, texture dissimilarity, and learning | |
| 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 |