Seismic Laboratory for Imaging and Modeling

Ph.D. Student

Research Area: Machine learning, scientific computing, large-scale inverse problems, PDE-constrained optimization, applications to time-lapse seismic monitoring of carbon storage and sequestration.

Homepage: https://ziyiyin97.github.io

About me: Ziyi (Francis) Yin is originally from Dalian, China. He graduated from Emory University with a Bachelor's degree in Mathematics and Computer Science in May, 2019 and joined SLIM group as a Ph.D. student in August, 2019. He is currently pursuing a Ph.D. degree in Computational Science and Engineering.

Y. Zeng, Rafael Orozco, Ziyi Yin, and Felix J. Herrmann, “Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation”. 2024.
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Ziyi Yin, Mathias Louboutin, Olav Møyner, and Felix J. Herrmann, “Time-lapse full-waveform permeability inversion: a feasibility study”. 2024.
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Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements”. 2024.
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Abhinav Prakash Gahlot, Haoyun Li, Ziyi Yin, Rafael Orozco, and Felix J. Herrmann, “A Digital Twin for Geological Carbon Storage with Controlled Injectivity”. 2024.
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Richard Rex, Ziyi Yin, and Felix J. Herrmann, “Velocity Continuation for Common Image Gathers with Fourier Neural Operators”. 2024.
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Felix J. Herrmann, Abhinav Prakash Gahlot, Rafael Orozco, Ziyi Yin, and Haoyun Li, “DT4GCS –- Digital Twin for Geological CO2 Storage and Control”. 2024.
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Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Huseyin Tuna Erdinc, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “Digital Twins in the Era of Generative AI: Application to Geological CO 2 Storage”, HCMF Seminar. 2024.
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Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Generative AI for full-waveform variational inference”, Georgia Tech Geophysics Seminar. 2024.
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Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “WISE: full-Waveform variational Inference via Subsurface Extensions”, Geophysics, 2024.
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Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Grant Bruer, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “An Uncertainty-Aware Digital Twin for Geological Carbon Storage”, in SIAM Conference on Uncertainty Quantification, 2024.
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Ziyi Yin, Mathias Louboutin, Olav Møyner, and Felix J. Herrmann, “End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model”, ML4SEISMIC Partners Meeting. 2023.
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Haoyun Li, Ziyi Yin, Olav Møyner, and Felix J. Herrmann, “Maximizing CO2 injectivity within fracture pressure”, ML4SEISMIC Partners Meeting. 2023.
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Richard Rex, Thomas J. Grady II, Rishi Khan, Ziyi Yin, and Felix J. Herrmann, “Large-scale parametric PDE approximations with model-parallel Fourier neural operators”, ML4SEISMIC Partners Meeting. 2023.
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Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Solving PDE-based inverse problems with learned surrogates and constraints”, HotCSE Seminar. 2023.
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Abhinav Prakash Gahlot, Mathias Louboutin, Ziyi Yin, and Felix J. Herrmann, “Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model”, ML4SEISMIC Partners Meeting. 2023.
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Ziyi Yin, “Introduction to Seismic Laboratory for Imaging and Modeling”, CSE Student Recruiting Event. 2023.
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Abhinav Prakash Gahlot, Ting-ying Yu, Rafael Orozco, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “Monitoring subsurface CO2 plumes with learned sequential Bayesian inference”, ML4SEISMIC Partners Meeting. 2023.
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Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “WISE: Full-waveform Inference with Subsurface Extensions”, ML4SEISMIC Partners Meeting. 2023.
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Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Solving multiphysics-based inverse problems with learned surrogates and constraints”, Advanced Modeling and Simulation in Engineering Sciences, vol. 10, 2023.
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Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Rafael Orozco, Ziyi Yin, and Felix J. Herrmann, “Inference of CO2 flow patterns – a feasibility study”, in Neural Information Processing Systems (NeurIPS), 2023.
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