[CCoE Notice] Dissertation Announcement.

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[Dissertation Defense Announcement at the Cullen College of Engineering]
Geophysical Inversion Enhanced by Deep Learning
Yuchen Jin

December 6, 2022; 1:00 PM - 4:00 PM (CST)
Location: ECE Large Conference room, D N328, Eng Bld 1
Microsoft Teams: Meeting Link<https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_OTY0MTVjZTMtMDJhMC00MmYyLWFlYjItZDdiYTM4NTNhMGJi*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*22ff6130b1-29eb-47cd-9377-a33a92edd281*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!DnYkstgXMZHRQxIg4EOzrh1X6_MCP-pROlIDzxcJXGtvzFJR9WZt1rjr0-lnrJzVuEv_3NpLPoKxt1jhDiGvcSd0nwI$ >

Committee Chair:
Jiefu Chen, Ph.D.

Committee Members:
Xuqing Wu, Ph.D. | Han Zhu, Ph.D. | Xiaonan Shan, Ph.D. | David R. Jackson, Ph.D | Weichang Li, Ph.D.

Abstract

Inversion is an essential technology for understanding geophysical data. In industrial practice, geophysical inversion is mainly used for high-resolution subsurface imaging. The reconstructed geological parameters include velocity, permittivity, permeability, and resistivity. The inversion results reveal interpretable features of subsurface structures and contribute to reservoir monitoring, logging while drilling, and other applications. Conventionally, solving the geophysical inversion relies on calculating the rigorous forward modeling function. Strictly following physics rules, the rigorous inversion can provide accurate model parameters. However, since the observations obtained by geophysical surveys are usually limited, geophysical inversions are usually highly non-linear and underdetermined. The complicated physical constraints may lead to local minima and slow convergence. And the rigorous numerical methods may demand a lot of computational resources. Therefore, using deep learning to enhance geophysical inversion has recently been an important topic.

This study mainly focuses on deep-learning-enhanced geophysical inversion with limited training data. Deep learning can fill the blank of the domain experience during the inversion. The deep learning approach can learn the specific data pattern from the historical data and provide different enhancements like data augmentation, data completion, and computing acceleration. However, many deep-learning approaches depend highly on a large amount of training data, which may be unavailable in field surveys. This work explores several deep-learning enhancement approaches for geophysical inversion with limited training data. The proposed methods are designed for solving different problems, including (1) the observation enhancement, (2) the end-to-end inversion surrogate, and (3) the end-to-end forward modeling surrogate. The physics rules are incorporated with the deep learning approaches by algorithms, regularizations, or constraints. For some problems, sensitivity analysis and uncertainty quantification are also studied. The experiment results show the promising performance of the proposed methods.

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