[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128541-en":3,"doc-seo-128541-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128541,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Approaches for Solving Subsurface Inverse Problems","Machine learning approaches address a key challenge in geotechnical engineering design: estimating subsurface physical properties such as shear stiffness and shear-wave velocity (Vs) for seismic site characterization. The work focuses on non-invasive surface-wave techniques that typically follow acquisition, processing, and inversion workflows. Emphasis is placed on the inversion step, which is difficult due to ill-posedness and non-uniqueness of subsurface inverse problems.","Copyright by  \nJodie Amberly Crocker 2023  \n1  \nThe Dissertation Committee for Jodie Amberly Crocker Certifies that this is the approved version of the following Dissertation:  \nMachine Learning Approaches for Solving Subsurface Inverse  \nProblems  \nCommittee:  \nKrishna Kumar, Supervisor  \nBrady R. Cox  \nEllen M. Rathje  \nKenneth H. Stokoe II  \nMichael J. Pyrcz  \nMachine Learning Approaches for Solving Subsurface Inverse  \nProblems  \nby  \nJodie Amberly Crocker  \nDissertation  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDoctor of Philosophy  \nThe University of Texas at Austin August 2023  \nDedication  \nTo my mother, for her unending support, to my partner, for his endless love and encouragement, and in memory of my father.  \nAcknowledgements  \nI would first like to acknowledge my advisor, Professor Krishna Kumar, for his support throughout my doctoral research. Throughout my Ph.D, Dr. Kumar has provided me with the freedom and resources to accomplish my goals and explore new topics related to machine learning and site characterization. In particular, I will always be grateful for his endless encouragement (and patience!) towards me while I learned how to write codes and develop machine learning models, as well as his support and advice regarding mentoring others.  \nNext, I would like to thank Professor Brady Cox for serving as my advisor throughout my master’s degree and continuing to support me as a member of my committee. Without him, I would not have discovered my interest in seismic site characterization, nor would I have explored non-traditional approaches to it, such as using machine learning models. Additionally, I am grateful for the mentorship opportunities he provided me, as they have allowed me to grow as both a researcher and an engineer.  \nI would also like to thank my committee members for their support and feedback throughout the development of this dissertation. In particular, Professor Ellen Rathje for her guidance, expertise, and encouragement. Professor Kenneth Stokoe, for his interest and enthusiasm in my work. And Professor Michael Pyrcz, for providing a geostatistician’s perspective on my work.  \nTo the other faculty members at The University of Texas at Austin, including Professor Robert Gilbert, Professor Chadi El Mohtar, Professor Jorge Zornberg, Professor Clark Wilson, Professor Steve Grand, Professor Maura Borrego, and Dr. Michelle Gaston. It has been a pleasure attending each of their courses and lectures, and their enthusiasm in  \nthe classroom made it a joy to learn a variety of topics in geotechnical engineering, geology, and pedagogy.  \nI would also like to acknowledge the colleagues I have worked with throughout the years. To those I met in Dr. Cox’s research group, including Mohamad Hallal, who mentored me when I first arrived at UT and was always willing to lend a hand. To Joseph Vantassel, who mentored me when I joined Dr. Kumar’s research group, and who patiently reassured me every time I messed up our shared GitHub repos. To Michael Yust, who showed me what to do (and more importantly, what not to do) when field testing. To Ugur Arslan, for his unrelenting optimism while we ran wave propagation simulations and inversions for an entire year. To those I met in Dr. Kumar’s research group, including Qiuyu (Amber) Wang, Anusha Vajapeyajula, Yongjin Choi, Cheng-His Hsiao, Chahak Mehta, and Brent Sordo. I will miss our group meetings and chats and will always be grateful for the warm welcome you all gave me when I joined the group.  \nI would like to extend my thanks to the friends I have made at UT Austin, as they have made this a memorable experience, including Dawie, Behdad, Benchen, Shradha, Steve, Olaide, Jingwen, Kangwei, and many others. And to Jiali Han, for always greeting everyone with a smile; to Chihun Sung, for all the “short” 2-hour coffee chats; to Thiago, Eduardo, and Fumaça,","cbCaisCZDwgoJt00","https://ap.wps.com/l/cbCaisCZDwgoJt00","pdf",4029923,1,136,"English","en",105,"# Dedication\n# Acknowledgements\n# Abstract\n## Problem context: subsurface property estimation\n## Workflow: acquisition, processing, inversion","[{\"question\":\"Why is subsurface inverse modeling difficult in this dissertation?\",\"answer\":\"The inversion step is challenging because subsurface inverse problems are ill-posed and non-unique, making reliable solutions hard to obtain.\"},{\"question\":\"What subsurface properties are targeted for estimation?\",\"answer\":\"The dissertation targets physical properties such as subsurface shear stiffness and shear-wave velocity (Vs) for seismic site characterization.\"},{\"question\":\"Why do the approaches emphasize non-invasive surface-wave methods?\",\"answer\":\"Non-invasive techniques are preferred because they are relatively inexpensive, quick to perform, and easier than invasive methods, while still supporting inversion workflows.\"}]","Machine Learning Approaches for Solving Subsurface Inverse Problems | 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