@article{FNO_one,
  author       = {Zongyi Li and
                  Nikola B. Kovachki and
                  Kamyar Azizzadenesheli and
                  Burigede Liu and
                  Kaushik Bhattacharya and
                  Andrew M. Stuart and
                  Anima Anandkumar},
  title        = {Fourier Neural Operator for Parametric Partial Differential Equations},
  journal      = {CoRR},
  volume       = {abs/2010.08895},
  year         = {2020},
  url          = {https://arxiv.org/abs/2010.08895},
  eprinttype    = {arXiv},
  eprint       = {2010.08895},
  timestamp    = {Wed, 21 Oct 2020 12:11:48 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2010-08895.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{li2020neural,
  title={Neural operator: Graph kernel network for partial differential equations},
  author={Li, Zongyi and Kovachki, Nikola and Azizzadenesheli, Kamyar and Liu, Burigede and Bhattacharya, Kaushik and Stuart, Andrew and Anandkumar, Anima},
  journal={arXiv preprint arXiv:2003.03485},
  year={2020}
}

@article{Bas_TV,
    author = {Peters, Bas and Herrmann, Felix J.},
    title = {Constraints versus penalties for edge-preserving full-waveform inversion},
    journal = {The Leading Edge},
    volume = {36},
    number = {1},
    pages = {94-100},
    year = {2017},
    month = {01},
    abstract = {Full-waveform inversion is challenging in complex geologic areas. Even when provided with an accurate starting model, the inversion algorithms often struggle to update the velocity model. Compared with other areas in applied geophysics, including prior information in full-waveform inversion is still in its relative infancy. In part, this is due to the fact that it is difficult to incorporate prior information that relates to geologic settings where strong discontinuities in the velocity model dominate, because these settings call for nonsmooth regularizations. We tackle this problem by including constraints on the spatial variations and value ranges of the inverted velocities, as opposed to adding penalties to the objective, which is more customary in mainstream geophysical inversion. By demonstrating the lack of predictability of edge-preserving inversion when the regularization is in the form of an added penalty term, we advocate the inclusion of constraints instead. Our examples show that the latter leads to more predictable results and to significant improvements in the delineation of salt bodies when these constraints are relaxed gradually in combination with extending the search space to approximately fit the observed data but not the noise.},
    issn = {1070-485X},
    doi = {10.1190/tle36010094.1},
    url = {https://doi.org/10.1190/tle36010094.1},
    eprint = {https://pubs.geoscienceworld.org/seg/tle/article-pdf/36/1/94/7435717/tle36010094.1.pdf},
}

@article{BG,
  author = "E. Jones, C. and A. Edgar, J. and I. Selvage, J. and Crook, H.",
  title = "Building Complex Synthetic Models to Evaluate Acquisition Geometries and Velocity Inversion Technologies", 
  journal= "In 74th EAGE Conference and Exhibition Incorporating EUROPEC 2012",
  year = "2012",
  volume = "",
  number = "",
  pages = "cp–293",
  doi = "https://doi.org/10.3997/2214-4609.20148575",
  url = "https://www.earthdoc.org/content/papers/10.3997/2214-4609.20148575",
  publisher = "European Association of Geoscientists \& Engineers",
  issn = "2214-4609",
  type = "",
  eid = "cp-293-00580",
}


@article{li2024physics,
  title={Physics-informed neural operator for learning partial differential equations},
  author={Li, Zongyi and Zheng, Hongkai and Kovachki, Nikola and Jin, David and Chen, Haoxuan and Liu, Burigede and Azizzadenesheli, Kamyar and Anandkumar, Anima},
  journal={ACM/JMS Journal of Data Science},
  volume={1},
  number={3},
  pages={1--27},
  year={2024},
  publisher={ACM New York, NY}
}

@inproceedings{li2024multi,
  title={Multi-Resolution Active Learning of Fourier Neural Operators},
  author={Li, Shibo and Yu, Xin and Xing, Wei and Kirby, Robert and Narayan, Akil and Zhe, Shandian},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={2440--2448},
  year={2024},
  organization={PMLR}
}

@article{RODA2020221,
title = {Bayesian inference for dynamical systems},
journal = {Infectious Disease Modelling},
volume = {5},
pages = {221-232},
year = {2020},
issn = {2468-0427},
doi = {https://doi.org/10.1016/j.idm.2019.12.007},
url = {https://www.sciencedirect.com/science/article/pii/S2468042719300478},
author = {Weston C. Roda},
keywords = {Bayesian, Inference, Model fitting, Data, Dynamical system, Mathematical model},
abstract = {Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed methodology. This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian inference and it covers utilizing different distributions, Markov Chain Monte Carlo (MCMC) sampling, obtaining credible intervals for parameters, and prediction intervals for solutions. A logistic growth example is given to illustrate the methodology.}
}

@article{kovachki2023neural,
  title={Neural operator: Learning maps between function spaces with applications to pdes},
  author={Kovachki, Nikola and Li, Zongyi and Liu, Burigede and Azizzadenesheli, Kamyar and Bhattacharya, Kaushik and Stuart, Andrew and Anandkumar, Anima},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={89},
  pages={1--97},
  year={2023}
}

@article{esser2018total,
  title={Total variation regularization strategies in full-waveform inversion},
  author={Esser, Ernie and Guasch, Lluis and van Leeuwen, Tristan and Aravkin, Aleksandr Y and Herrmann, Felix J},
  journal={SIAM Journal on Imaging Sciences},
  volume={11},
  number={1},
  pages={376--406},
  year={2018},
  publisher={SIAM}
}

@article{markov_neural,
  author       = {Zongyi Li and
                  Nikola B. Kovachki and
                  Kamyar Azizzadenesheli and
                  Burigede Liu and
                  Kaushik Bhattacharya and
                  Andrew M. Stuart and
                  Anima Anandkumar},
  title        = {Markov Neural Operators for Learning Chaotic Systems},
  journal      = {CoRR},
  volume       = {abs/2106.06898},
  year         = {2021},
  url          = {https://arxiv.org/abs/2106.06898},
  eprinttype    = {arXiv},
  eprint       = {2106.06898},
  timestamp    = {Tue, 15 Jun 2021 16:35:15 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2106-06898.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{yin2023solving,
  title={Solving multiphysics-based inverse problems with learned surrogates and constraints},
  author={Yin, Ziyi and Orozco, Rafael and Louboutin, Mathias and Herrmann, Felix J},
  journal={Advanced Modeling and Simulation in Engineering Sciences},
  volume={10},
  number={1},
  pages={14},
  year={2023},
  publisher={Springer}
}

@article{grady2023model,
  title={Model-parallel Fourier neural operators as learned surrogates for large-scale parametric PDEs},
  author={Grady, Thomas J and Khan, Rishi and Louboutin, Mathias and Yin, Ziyi and Witte, Philipp A and Chandra, Ranveer and Hewett, Russell J and Herrmann, Felix J},
  journal={Computers \& Geosciences},
  volume={178},
  pages={105402},
  year={2023},
  publisher={Elsevier}
}

@article{kovachki2023neural,
  title={Neural operator: Learning maps between function spaces with applications to pdes},
  author={Kovachki, Nikola and Li, Zongyi and Liu, Burigede and Azizzadenesheli, Kamyar and Bhattacharya, Kaushik and Stuart, Andrew and Anandkumar, Anima},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={89},
  pages={1--97},
  year={2023}
}

@article{park2024dynamical,
  title={When are dynamical systems learned from time series data statistically accurate?},
  author={Park, Jeongjin and Yang, Nicole T and Chandramoorthy, Nisha},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={43975--44008},
  year={2024}
}

@article{park2025reduced,
  title={A reduced-order derivative-informed neural operator for subsurface fluid-flow},
  author={Park, Jeongjin and Bruer, Grant and Erdinc, Huseyin Tuna and Gahlot, Abhinav Prakash and Herrmann, Felix J},
  journal={arXiv preprint arXiv:2509.13620},
  year={2025}
}

@mastersthesis{park2024can,
  title={Can a Neural ODE Learn a Chaotic System?},
  author={Park, Jeongjin},
  year={2024},
  school={Georgia Institute of Technology}
}

@article{qin2024toward,
  title={Toward a Better Understanding of Fourier Neural Operators: Analysis and Improvement from a Spectral Perspective},
  author={Qin, Shaoxiang and Lyu, Fuyuan and Peng, Wenhui and Geng, Dingyang and Wang, Ju and Gao, Naiping and Liu, Xue and Wang, Liangzhu Leon},
  journal={arXiv preprint arXiv:2404.07200},
  year={2024}
}
@article{gao2024adaptive,
  title={Adaptive operator learning for infinite-dimensional bayesian inverse problems},
  author={Gao, Zhiwei and Yan, Liang and Zhou, Tao},
  journal={SIAM/ASA Journal on Uncertainty Quantification},
  volume={12},
  number={4},
  pages={1389--1423},
  year={2024},
  publisher={SIAM}
}

@article{sheriffdeen2019accelerating,
  title={Accelerating PDE-constrained inverse solutions with deep learning and reduced order models},
  author={Sheriffdeen, Sheroze and Ragusa, Jean C and Morel, Jim E and Adams, Marvin L and Bui-Thanh, Tan},
  journal={arXiv preprint arXiv:1912.08864},
  year={2019}
}

@inproceedings{wu2024uncertainty,
  title={Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution},
  author={Wu, Tailin and Neiswanger, Willie and Zheng, Hongtao and Ermon, Stefano and Leskovec, Jure},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={1},
  pages={320--328},
  year={2024}
}

@article{rosofsky2023applications,
  title={Applications of physics informed neural operators},
  author={Rosofsky, Shawn G and Al Majed, Hani and Huerta, EA},
  journal={Machine Learning: Science and Technology},
  volume={4},
  number={2},
  pages={025022},
  year={2023},
  publisher={IOP Publishing}
}

@article{o2024derivative,
  title={Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning},
  author={O'Leary-Roseberry, Thomas and Chen, Peng and Villa, Umberto and Ghattas, Omar},
  journal={Journal of Computational Physics},
  volume={496},
  pages={112555},
  year={2024},
  publisher={Elsevier}
}

@article{go2023accelerating,
  title={Accelerating Bayesian Optimal Experimental Design with Derivative-Informed Neural Operators},
  author={Go, Jinwoo and Chen, Peng},
  journal={arXiv preprint arXiv:2312.14810},
  year={2023}
}

@article{qiu2024derivative,
  title={Derivative-enhanced Deep Operator Network},
  author={Qiu, Yuan and Bridges, Nolan and Chen, Peng},
  journal={arXiv preprint arXiv:2402.19242},
  year={2024}
}

@article{o2022derivative,
  title={Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs},
  author={O’Leary-Roseberry, Thomas and Villa, Umberto and Chen, Peng and Ghattas, Omar},
  journal={Computer Methods in Applied Mechanics and Engineering},
  volume={388},
  pages={114199},
  year={2022},
  publisher={Elsevier}
}

@article{wu2023large,
  title={Large-scale Bayesian optimal experimental design with derivative-informed projected neural network},
  author={Wu, Keyi and O’Leary-Roseberry, Thomas and Chen, Peng and Ghattas, Omar},
  journal={Journal of Scientific Computing},
  volume={95},
  number={1},
  pages={30},
  year={2023},
  publisher={Springer}
}

@article{chen2019hessian,
  title={Hessian-based sampling for high-dimensional model reduction},
  author={Chen, Peng and Ghattas, Omar},
  journal={International Journal for Uncertainty Quantification},
  volume={9},
  number={2},
  year={2019},
  publisher={Begel House Inc.}
}


@inproceedings{yin2022learned,
  title={Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators},
  author={Yin, Ziyi and Siahkoohi, Ali and Louboutin, Mathias and Herrmann, Felix J},
  booktitle={Second International Meeting for Applied Geoscience \& Energy},
  pages={467--472},
  year={2022},
  organization={Society of Exploration Geophysicists and American Association of Petroleum~…}
}

@article{cai2021physics,
  title={Physics-informed neural networks (PINNs) for fluid mechanics: A review},
  author={Cai, Shengze and Mao, Zhiping and Wang, Zhicheng and Yin, Minglang and Karniadakis, George Em},
  journal={Acta Mechanica Sinica},
  volume={37},
  number={12},
  pages={1727--1738},
  year={2021},
  publisher={Springer}
}

@book{constantine2015active,
  title={Active subspaces: Emerging ideas for dimension reduction in parameter studies},
  author={Constantine, Paul G},
  year={2015},
  publisher={SIAM}
}

@article{benner2015survey,
  title={A survey of projection-based model reduction methods for parametric dynamical systems},
  author={Benner, Peter and Gugercin, Serkan and Willcox, Karen},
  journal={SIAM review},
  volume={57},
  number={4},
  pages={483--531},
  year={2015},
  publisher={SIAM}
}

@book{quarteroni2015reduced,
  title={Reduced basis methods for partial differential equations: an introduction},
  author={Quarteroni, Alfio and Manzoni, Andrea and Negri, Federico},
  volume={92},
  year={2015},
  publisher={Springer}
}

@article{zahm2022certified,
  title={Certified dimension reduction in nonlinear Bayesian inverse problems},
  author={Zahm, Olivier and Cui, Tiangang and Law, Kody and Spantini, Alessio and Marzouk, Youssef},
  journal={Mathematics of Computation},
  volume={91},
  number={336},
  pages={1789--1835},
  year={2022}
}

@article{constantine2014active,
  title={Active subspace methods in theory and practice: applications to kriging surfaces},
  author={Constantine, Paul G and Dow, Eric and Wang, Qiqi},
  journal={SIAM Journal on Scientific Computing},
  volume={36},
  number={4},
  pages={A1500--A1524},
  year={2014},
  publisher={SIAM}
}

@article{son2021sobolev,
  title={Sobolev training for physics informed neural networks},
  author={Son, Hwijae and Jang, Jin Woo and Han, Woo Jin and Hwang, Hyung Ju},
  journal={arXiv preprint arXiv:2101.08932},
  year={2021}
}

@book{beck2014introduction,
  title={Introduction to nonlinear optimization: Theory, algorithms, and applications with MATLAB},
  author={Beck, Amir},
  year={2014},
  publisher={SIAM}
}

@article{karniadakis2021physics,
  title={Physics-informed machine learning},
  author={Karniadakis, George Em and Kevrekidis, Ioannis G and Lu, Lu and Perdikaris, Paris and Wang, Sifan and Yang, Liu},
  journal={Nature Reviews Physics},
  volume={3},
  number={6},
  pages={422--440},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{lu2021learning,
  title={Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators},
  author={Lu, Lu and Jin, Pengzhan and Pang, Guofei and Zhang, Zhongqiang and Karniadakis, George Em},
  journal={Nature machine intelligence},
  volume={3},
  number={3},
  pages={218--229},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{zhang2022fourier,
  title={Fourier neural operator for solving subsurface oil/water two-phase flow partial differential equation},
  author={Zhang, Kai and Zuo, Yuande and Zhao, Hanjun and Ma, Xiaopeng and Gu, Jianwei and Wang, Jian and Yang, Yongfei and Yao, Chuanjin and Yao, Jun},
  journal={Spe Journal},
  volume={27},
  number={03},
  pages={1815--1830},
  year={2022},
  publisher={OnePetro}
}

@article{wen2022u,
  title={U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow},
  author={Wen, Gege and Li, Zongyi and Azizzadenesheli, Kamyar and Anandkumar, Anima and Benson, Sally M},
  journal={Advances in Water Resources},
  volume={163},
  pages={104180},
  year={2022},
  publisher={Elsevier}
}

@article{li2020fourier,
  title={Fourier neural operator for parametric partial differential equations},
  author={Li, Zongyi and Kovachki, Nikola and Azizzadenesheli, Kamyar and Liu, Burigede and Bhattacharya, Kaushik and Stuart, Andrew and Anandkumar, Anima},
  journal={arXiv preprint arXiv:2010.08895},
  year={2020}
}

@article{li2020multipole,
  title={Multipole graph neural operator for parametric partial differential equations},
  author={Li, Zongyi and Kovachki, Nikola and Azizzadenesheli, Kamyar and Liu, Burigede and Stuart, Andrew and Bhattacharya, Kaushik and Anandkumar, Anima},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6755--6766},
  year={2020}
}

@article{gahlot2024uncertainty,
  title={An uncertainty-aware digital shadow for underground multimodal CO2 storage monitoring},
  author={Gahlot, Abhinav Prakash and Orozco, Rafael and Yin, Ziyi and Herrmann, Felix J},
  journal={arXiv preprint arXiv:2410.01218},
  year={2024}
}

@article{cui2021data,
  title={Data-free likelihood-informed dimension reduction of Bayesian inverse problems},
  author={Cui, Tiangang and Zahm, Olivier},
  journal={Inverse Problems},
  volume={37},
  number={4},
  pages={045009},
  year={2021},
  publisher={IOP Publishing}
}

@article{chandra2025fourier,
  title={Fourier Neural Operator based surrogates for $ CO\_2 $ storage in realistic geologies},
  author={Chandra, Anirban and Koch, Marius and Pawar, Suraj and Panda, Aniruddha and Azizzadenesheli, Kamyar and Snippe, Jeroen and Alpak, Faruk O and Hariri, Farah and Etienam, Clement and Devarakota, Pandu and others},
  journal={arXiv preprint arXiv:2503.11031},
  year={2025}
}

@article{jutuldarcy_ecmor_2024,
   author = "M{\o}yner, O.",
   title = "JutulDarcy.jl - a Fully Differentiable High-Performance Reservoir Simulator Based on Automatic Differentiation", 
   year = "2024",
   volume = "2024",
   number = "1",
   pages = "1-9",
   doi = "https://doi.org/10.3997/2214-4609.202437111",
   publisher = "European Association of Geoscientists \& Engineers",
   issn = "2214-4609",
}

@software{jutuldarcyrules,
  author       = {Ziyi Yin and
                  Grant Bruer and
                  Mathias Louboutin},
  title        = {slimgroup/JutulDarcyRules.jl: v0.2.8},
  month        = may,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v0.2.8},
  doi          = {10.5281/zenodo.11228539},
  url          = {https://doi.org/10.5281/zenodo.11228539},
}

@book{tarantola1984inverse,
  author    = {Tarantola, Albert},
  title     = {Inverse Problem Theory},
  publisher = {Elsevier},
  year      = {1984}
}

@article{virieux2009overview,
  author  = {Virieux, Jean and Operto, Stéphane},
  title   = {An Overview of Full-Waveform Inversion in Exploration Geophysics},
  journal = {Geophysics},
  volume  = {74},
  number  = {6},
  pages   = {WCC1--WCC26},
  year    = {2009}
}

@article{symes2008migration,
  author  = {Symes, William W.},
  title   = {Migration Velocity Analysis and Waveform Inversion},
  journal = {Geophysical Prospecting},
  volume  = {56},
  number  = {6},
  pages   = {765--790},
  year    = {2008}
}

@article{louboutin2017full,
  author  = {Louboutin, Mathias and Lange, Michael and Luporini, Fabio and Kukreja, Navjot and Witte, Philipp A. and Kelly, Paul H.J. and Gorman, Gerard J. and Herrmann, Felix J.},
  title   = {Full-Waveform Inversion with Devito},
  journal = {SEG Technical Program Expanded Abstracts},
  pages   = {1515--1520},
  year    = {2017}
}

@inproceedings{siahkoohi2022velocity,
  title={Velocity continuation with Fourier neural operators for accelerated uncertainty quantification},
  author={Siahkoohi, Ali and Louboutin, Mathias and Herrmann, Felix J},
  booktitle={SEG International Exposition and Annual Meeting},
  pages={D011S092R004},
  year={2022},
  organization={SEG}
}

@inproceedings{ma2025velocity,
  title={Velocity model building from seismic images using a Convolutional Neural Operator},
  author={Ma, Xiao and Alkhalifah, Tariq},
  booktitle={EAGE Workshop on Enhancing Subsurface Practices using AI/ML},
  volume={2025},
  number={1},
  pages={1--5},
  year={2025},
  organization={European Association of Geoscientists \& Engineers}
}

@article{geng2022deep,
  title={Deep learning for velocity model building with common-image gather volumes},
  author={Geng, Zhicheng and Zhao, Zeyu and Shi, Yunzhi and Wu, Xinming and Fomel, Sergey and Sen, Mrinal},
  journal={Geophysical Journal International},
  volume={228},
  number={2},
  pages={1054--1070},
  year={2022},
  publisher={Oxford University Press}
}

@article{orozco2024machine,
  title={Machine learning-enabled velocity model building with uncertainty quantification},
  author={Orozco, Rafael and Erdinc, Huseyin Tuna and Zeng, Yunlin and Louboutin, Mathias and Herrmann, Felix J},
  journal={arXiv preprint arXiv:2411.06651},
  year={2024}
}

@article{yin2024wise,
  title={WISE: Full-waveform variational inference via subsurface extensions},
  author={Yin, Ziyi and Orozco, Rafael and Louboutin, Mathias and Herrmann, Felix J},
  journal={Geophysics},
  volume={89},
  number={4},
  pages={A23--A28},
  year={2024},
  publisher={Society of Exploration Geophysicists}
}

@article{yin2025wiser,
  title={WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction},
  author={Yin, Ziyi and Orozco, Rafael and Herrmann, Felix J},
  journal={Geophysics},
  volume={90},
  number={2},
  pages={A1--A7},
  year={2025},
  publisher={Society of Exploration Geophysicists}
}

@article{witteJUDI2019,
author = {Philipp A. Witte and Mathias Louboutin and Navjot Kukreja and Fabio Luporini and Michael Lange and Gerard J. Gorman and Felix J. Herrmann},
title = {A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia},
journal = {GEOPHYSICS},
volume = {84},
number = {3},
pages = {F57-F71},
year = {2019},
doi = {10.1190/geo2018-0174.1},
URL = {https://doi.org/10.1190/geo2018-0174.1},
eprint = {https://doi.org/10.1190/geo2018-0174.1}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@software{mathias_louboutin_2023_7785440,
  author       = {Mathias Louboutin and
                  Philipp Witte and
                  Ziyi Yin and
                  Henryk Modzelewski and
                  Kerim and
                  Carlos da Costa and
                  Peterson Nogueira},
  title        = {slimgroup/JUDI.jl: v3.2.3},
  month        = mar,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v3.2.3},
  doi          = {10.5281/zenodo.7785440},
  url          = {https://doi.org/10.5281/zenodo.7785440},
}

@inproceedings{erdinc2025power,
  author = {Erdinc, Huseyin Tuna and Zeng, Yunlin and Gahlot, Abhinav Prakash and Herrmann, Felix J.},
  title  = {Power-scaled Bayesian inference with score-based generative models},
  booktitle = {SEG Technical Program Expanded Abstracts},
  volume  = {44},
  pages  = {21--25},
  year  = {2025},
  doi  = {10.1190/image2025-4305502.1},
  url = {https://doi.org/10.1190/image2025-4305502.1},
  note = {Also available as arXiv:2504.10807}
}