@unpublished {li2026IMAGEsca,
	title = {JUDIAgent: a scientific coding agent for JUDI workflows in wave-equation imaging},
	year = {2026},
	month = {3},
	abstract = {We present JUDIAgent, a scientific coding assistant for JUDI.jl, an open-source Julia framework for wave-equation-based seismic modeling, imaging, and inversion. In seismic experimentation, the practical difficulty is not only writing executable code, but assembling a complete seismic workflow with the required physical model, acquisition geometry, modeling or migration operator, and saved outputs needed for inspection. JUDIAgent addresses this problem by retrieving JUDI examples, generating Julia code, running the code in the target environment, and checking whether the generated script contains the requested workflow pieces and outputs, such as acquisition geometry and saved figures. We demonstrate the system with validated 2D forward-modeling and reverse-time migration (RTM) examples. These case studies show that, for JUDI-based seismic scripts, domain-aware validation can turn a lightweight user request into a more complete experimental workflow by detecting missing workflow steps that are not captured by runtime correctness alone and by making the resulting scripts easier to inspect and revise.},
	keywords = {Agentic AI, devito, FWI, IMAGE, Imaging, JUDI, RTM, scientific coding agent, SEG},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/li2026IMAGEsca/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/li2026IMAGEsca/IMAGE_Haoyun.pdf},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {yin2022TRANSFORMjulia,
	title = {Julia for Geoscience},
	year = {2022},
	month = {04},
	abstract = {In this tutorial, we will introduce the Julia programming language to the geoscience community, covering topics such as I/O, data processing, inversion, and machine learning. We will begin by installing Julia and relevant packages. Through a series of tutorials, we will demonstrate Julia{\textquoteright}s abstraction power and show how to load and plot data, write your own functions/operators, form and solve a geophysical inverse problem, and demonstrate how to integrate wave-equation solvers in Julia with machine learning frameworks. The intent of this presentation is to provide an introductory level tutorial that will be useful to members of the geoscience community.},
	keywords = {JOLI, JUDI, Julia, machine learning, SLIM, software, TRANSFORM},
	url = {https://transform.softwareunderground.org/2022-julia-for-geoscience},
	url2 = {https://www.youtube.com/watch?v=HyWfp3NzIbg},
	software = {https://github.com/slimgroup/SLIMTutorials},
	author = {Ziyi Yin and Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Henryk Modzelewski and Felix J. Herrmann}
}
@techreport {smithyman2014SEGjfw,
	title = {Joint full-waveform inversion of on-land surface and VSP data from the Permian Basin},
	number = {TR-EOAS-2014-4},
	year = {2014},
	month = {04},
	institution = {UBC},
	abstract = {Full-waveform Inversion is applied to generate a high-resolution model of P-wave velocity for a site in the Permian Basin, Texas, USA. This investigation jointly inverts seismic waveforms from a surface 3-D vibroseis surface seismic survey and a co-located 3-D Vertical Seismic Profiling (VSP) survey, which shared common source Vibration Points (VPs). The resulting velocity model captures features that were not resolvable by conventional migration velocity analysis.},
	keywords = {downhole receivers, Full-waveform inversion, land, seismic, vibroseis},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2014/smithyman2014SEGjfw/smithyman2014SEGjfw.html},
	author = {Brendan R. Smithyman and Bas Peters and Bryan DeVault and Felix J. Herrmann}
}
@conference {hennenfent2007SINBADjdn,
	title = {Just denoise: nonlinear recovery from randomly sampled data},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity-promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data.},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Gilles Hennenfent}
}
@conference {herrmann2007EAGEjda,
	title = {Just diagonalize: a curvelet-based approach to seismic amplitude recovery},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {In his presentation we present a nonlinear curvelet-based sparsity-promoting formulation for the recovery of seismic amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under wave propagation lead to a formulation of this recovery problem that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7555},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
