Improving the Interpretation of Magnetic Tensor Data Using Deep Learning

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Geophysics

Grantor

University of Houston

Year dc:date.issued

2023

Author dc:creator

• Barker, Keenan

Advisor dc:contributor.advisor

• Sun, Jiajia

Committee members dc:contributor.committeemember

• Zhu, Jennifer
• Wang, Guoquan

• Magnetic tensor data
• U-Net
• Kimberlite
• Machine learning
• Deep learning

Statement 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).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/14540

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/14540