Improving the Interpretation of Magnetic Tensor Data Using Deep Learning
| dc.contributor.advisor | Sun, Jiajia | |
| dc.contributor.committeeMember | Zhu, Jennifer | |
| dc.contributor.committeeMember | Wang, Guoquan | |
| dc.creator | Barker, Keenan | |
| dc.date.accessioned | 2023-06-14T17:36:23Z | |
| dc.date.created | May 2023 | |
| dc.date.issued | 2023-05-05 | |
| dc.date.updated | 2023-06-14T17:36:24Z | |
| dc.description.abstract | The accurate interpretation of magnetic tensor data can be difficult to perform without a strong knowledge of local geology and experience in reading magnetic data. I examined ways in which machine learning techniques can be applied to magnetic tensor data to automatically locate possible kimberlite targets and a method to sharpen smoothness based inversion models to provide a clearer image of the subsurface. While machine learning networks like the U-Net have shown success in other fields for image processing, these methods have not been used extensively in geophysics for magnetic interpretation. I trained the U-Net to predict kimberlite pipe locations by forward modelling magnetic susceptibility models to corresponding magnetic tensor data. I examined the use of different neural network architectures and methods for calculating loss to determine the effect on prediction accuracy. The U-Net was adapted in order to sharpen inversion models by changing from a two dimensional layer architecture to three dimensions. To train this second U-Net I first created a smoothing function which closely matches the effects of a smoothness based inversion and then applied this smoothing function to three dimensional magnetic susceptibility models. I also compared the effectiveness of using a general smoothing function against a smoothing function specifically designed to match a smoothness inversion. This study shows that the use of the U-Net architecture in the field of magnetics shows great promise in the areas of automatically detecting targets and sharpening inverted models. | |
| dc.description.department | Earth and Atmospheric Sciences, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10657/14540 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Magnetic tensor data | |
| dc.subject | U-Net | |
| dc.subject | Kimberlite | |
| dc.subject | Machine learning | |
| dc.subject | Deep learning | |
| dc.title | Improving the Interpretation of Magnetic Tensor Data Using Deep Learning | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2025-05-01 | |
| local.embargo.terms | 2025-05-01 | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Earth and Atmospheric Sciences, Department of | |
| thesis.degree.discipline | Geophysics | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |