Deep Learning Enhanced Multi-physics Joint Inversion
| dc.contributor.advisor | Chen, Jiefu | |
| dc.contributor.committeeMember | Wu, Xuqing | |
| dc.contributor.committeeMember | Han, Zhu | |
| dc.contributor.committeeMember | Jackson, David R. | |
| dc.contributor.committeeMember | Hu, Wenyi | |
| dc.creator | Hu, Yanyan | |
| dc.creator.orcid | 0000-0001-7982-806X | |
| dc.date.accessioned | 2024-01-27T16:59:48Z | |
| dc.date.created | December 2023 | |
| dc.date.issued | 2023-12 | |
| dc.date.updated | 2024-01-27T16:59:48Z | |
| dc.description.abstract | Joint inversion has drawn considerable attention due to the availability of multiple geophysical datasets, ever-increasing computational resources, development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, where the information obtained from different models can complement each other. Traditionally, structural similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g. cross gradient). In this dissertation, we introduce a novel approach: a Deep Learning Enhanced (DLE) joint inversion framework. Within this framework, structural similarity is enforced using a well-trained deep neural network (DNN), enhancing the quality of joint inversion results through iterative improvements. The DNN is seamlessly integrated into existing independent inversion workflows, with the flexibility to extend its application to various multi-physics scenarios without requiring structural modifications. Within the DLE joint inversion framework, several key contributions are made. First, we design a double-channel U-Net for the simultaneous inversion of 2D DC resistivity data and seismic travel time. Extensive numerical experiments validate the efficacy of this method. Importantly, this learning-based approach exhibits impressive generalization capabilities when applied to datasets featuring diverse geological structures, sensing configurations, and nonconforming discretization. Second, we harness the power of deep perceptual loss as a regularization technique to further enhance structural similarity. Successive networks are trained with deep perceptual constraints, derived from a pre-trained network specializing in edge detection. The robustness of this approach is verified through experiments involving layered subsurface models, demonstrating its ability to jointly invert three types of geophysical data, including induced polarization data. Third, we simplify the DLE framework and apply it to tackle the challenging 3D joint inversion of magnetic and gravity gradient data. The proposed method is rigorously evaluated through synthetic and field cases, affirming its effectiveness and computational efficiency. In summary, this dissertation contributes to the advancement of multi-physics joint inversion by introducing the Deep Learning Enhanced framework. This innovative approach enhances both the accuracy and efficiency of geophysical inversion, promising broader applications and improved outcomes in the field. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Hu, Yanyan, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiuping Chen, Yueqin Huang, and Jiefu Chen. "A deep learning-enhanced framework for multiphysics joint inversion." Geophysics 88, no. 1 (2023): K13-K26; and in: Hu, Yanyan, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Yueqin Huang, and Jiefu Chen. "3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques." Geophysics 89, no. 1 (2023): 1-57. | |
| dc.identifier.uri | https://hdl.handle.net/10657/16224 | |
| 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. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Deep learning, joint inversion, multi-physics | |
| dc.title | Deep Learning Enhanced Multi-physics Joint Inversion | |
| 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 | 2025-12-01 | |
| local.embargo.terms | 2025-12-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |